Miracle-dz/UniFAR-FTUs huggingface dataset:
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

This study applies statistical process control and machine learning techniques to diagnose wind turbine faults and predict maintenance needs by analyzing 2.8 million sensor data collected from 31 wind turbines from 2015 to 2017 in Taiwan. Unlike previous studies that only relied on historical wind turbine data, this study analyzed the sensor data with practitioners’ insight by incorporating maintenance check list items into the data mining processes. We used Pareto analyses, scatter plots, and the cause and effect diagram to cluster and classify the failure types of wind turbines. In addition, control charts were used to establish a monitoring mechanism to track whether operation data are deviated from the controls (i.e., standard deviations) as a mean to detect wind turbine abnormalities. While statistical process control was applied to fault diagnosis, machine learning algorithms were used to predict maintenance needs of wind turbines. First, the density-based spatial clustering of applications with noise algorithm was used to classify abnormal-state wind turbine data from normal-state data. Then, random forest and decision tree algorithms were employed to construct the predictive models for wind turbine anomalies and tested with K-fold crossvalidation. The results indicate a high level of accuracy: 92.68% for the decision tree model, and 91.98% for the random forest model. The study demonstrates that, by data mining and modeling, the failures of wind turbines can be detected, and the maintenance needs of parts can be predicted. Model results may provide technicians early warnings, improve equipment efficient, and decrease system downtime of wind turbine operation. INDEX TERMS Decision trees, fault diagnosis, machine learning, predictive maintenance, random forest, statistical process control, wind energy.
Feature Extraction of Echo Signal of Weld Defect Guided Waves Based on Sparse Representation

In order to accurately detect the location of weld defect from strong background noises, a new method incorporating Morlet wavelet basis atom into sparse representation theory is investigated and it is suitable for detecting the weld defect feature from the guided wave echo signal. After determining the matching basis function, the split augmented Lagrangian shrinkage algorithm is introduced to solve the basis pursuit denoising problem. The echo signal can be intuitively represented in the sparse coefficients, then the defect signal can be effectively extracted from strong background noises. Both the simulated studies and the real applications of experimental signals show that this method is precise and stable, and can effectively suppress the influence of noises.
A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification

Wafer map analysis provides critical information for quality control and yield improvement tasks in semiconductor manufacturing. In particular, wafer patterns of gross failing areas (GFA) are important clues to identify the causes of relevant failures during the manufacturing process. In this work, a semi-supervised classification framework is proposed for wafer map analysis, and its application to wafer bin maps with GFA patterns classification is demonstrated. The Ladder network and the semi-supervised variational autoencoder are adopted to classify wafer bin maps in comparison with a standard convolutional neural network (CNN) model on two real-world datasets. The results have illustrated that two semi-supervised models are consistently and substantially better than the CNN model across various training data percentages by effective utilization of the unlabeled data. Active learning and pseudo labeling are also utilized to accelerate the learning curve.
Design of a hexapod robot using artificial intelligence for the routes of the peruvian andes

This paper presents an alternative solution to improve the locomotion system of a hexapod robot by artificial intelligence. Through an optimal design to achieve static stability, dynamic stability and optimize energy consumption through an autonomous system that is able to perform trajectories without any inconvenience. For the robot to move without flaws has certain restrictions in design (weight, size, materials, etc.) The hexapod has a high degree of movement and this allows many trajectories handle at the time of travel. Using sensors under certain working conditions we will obtain the necessary data and signals to satisfactorily comply with the hexapod robot design.
Static Block Floating-Point Quantization for Convolutional Neural Networks on FPGA

Convolutional neural networks (CNNs) have been widely applied in various computer vision and speech processing applications. However, the algorithmic complexity of CNNs hinders their deployment in embedded systems with limited memory and computational resources. This paper proposes static block floating-point (BFP) quantization, an effective approach involving Kullback-Leibler divergence, to determine the static shared exponents. Without need for retraining, the proposed approach is able to quantize CNNs to 8 bits with negligible accuracy loss. An FPGA-based hardware design with static BFP quantization is also proposed. Compared with 8-bit integer linear quantization, our experiments show that the hardware kernel based on static BFP quantization can achieve over 50% reduction in logic resources on an FPGA. Based on static BFP quantization, a tool implemented in the PyTorch framework is developed, which can automatically generate optimised configuration according to user requirements for given CNN models, where the entire optimization process takes only a few minutes on an Intel Xeon Silver 4110 CPU.
Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing involves information from what customers think is captured by an Electroencephalogram (EEG) device. Meanwhile, if the electrical signal information in the brain is corresponding to an understanding of one's psychology, it is called neuropsychology. This field consists of emotions, attention, and concentration variables. This research offered neuromarketing base on emotion and attention of EEG signal using Wavelet extraction and Recurrent Neural Networks. Measurement of EEG variable every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 5%.
Nanoscale SiGe Double Gate MOSFET (DG-MOSFET) for Analog/RF Circuits

A simulation study of double-gate SiGe channel based metal-oxide-semiconductor field-effect transistor (DG-MOSFET) for low power analog/RF circuits is presented. The proposed device has two gates which are taken in trenches vertically to form two channels to improve output drive current (I<inf>D</inf>). In addition to this, the DG-MOSFET also achieves substantially enhancement in transconductance (g<inf>m</inf>) and frequency characteristics (f<inf>t</inf> and f<inf>max</inf>). The simulation results of DG-MOSFET are compared with its conventional counterpart. At a gate-length of 60nm, DG-MOSFET gives 3.1 times improvement in I<inf>D</inf>, 2.9 times higher peak g<inf>m</inf>, 2 times increase in f<inf>t</inf> and 2.6 times higher f<inf>max</inf> as compare to conventional planar-counterpart (CP-MOSFET).
MHCherryPan. a novel model to predict the binding affinity of pan-specific class I HLA-peptide

The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans and thousands of HLA allelic versions exist [1]. Due to the high polymorphism of the HLA complex, it remains difficulties to accurately predict the binding affinity. In this paper, we present a new algorithm to combine a convolutional neural network and long short-term memory to solve this problem. Compared with other current popular models, our model achieved state-of the-art results.
Real-time Optimization of Continuous Catalytic Reforming Process with Hybrid Model

A real-time optimization method with the combination of simplified mechanism and deep belief neural network model for continuous catalytic reforming (CCR) process is presented this paper. The basic model of reforming reaction is established based on the simplified mechanism as well as the kinetic parameters are estimated with deep belief neural network(DBNN), and the objective functuon together the maximization of aromatics yield with the minimization of energy consumption is solved by the non-line optimizer. The hybrid mode, can not only reflect the physical characteristics of the CCR process, but also improve the model extrapolation ability, which is greatly guaranteed the efficiency and stability for the real-time optimization of CCR. Finally, the effectiveness of method proposed is validated through a case study on a industry CCR process.
Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

Information trustworthiness assessment on political social media discussions is crucial to maintain the order of society, especially during emergent situations. The polarity nature of political topics and the echo chamber effect by social media platforms allow for a deceptive and a dividing environment. During a political crisis, a vast amount of information is being propagated on social media, that leads up to a high level of polarization and deception by the beneficial parties. The traditional approaches to tackling misinformation on social media usually lack a comprehensive problem definition due to its complication. This paper proposes a probabilistic graphical model as a theoretical view on the problem of normal users credibility on social media during a political crisis, where polarization and deception are keys properties. Such noisy signals dramatically influence any attempts for misinformation detection. Hence, we introduce a causal Bayesian network, inspired by the potential main entities that would be part of the process dynamics. Our methodology examines the problem solution in a causal manner which considers the task of misinformation detection as a question of cause and effect rather than just a classification task. Our causality-based approach provides a practical road map for some sub-problems in real-world scenarios such as individual polarization level, misinformation detection, and sensitivity analysis of the problem. Moreover, it facilitates intervention simulations which would unveil both positive and negative effects on the deception level over the network.
Igniting the Maker Spirit: Design and Pilot Deployment of the Kappa Tangible Electronics Prototyping Kit

We introduce Kappa, an electronic tangible interface development kit designed to promote computational thinking for school students. Kappa enables students to create interactive games, animated stories and musical instruments using physical objects. We discuss the design considerations, the working mechanism and the need for such a toolkit in technology education. We also discuss results and observations from a pilot deployment of the kit in a school in urban India in a project-based learning context.
Deep Principal Correlated Auto-Encoders With Application to Imaging and Genomics Data Integration

In terms of complex diseases like schizophrenia, more and more studies are beginning to treat genetic variants and brain imaging phenotypes as an important factor. In this paper, a competent optimization model is exploited to overcome the weakness of deep canonical correlation analysis (DCCA). The model consists of principal component analysis (PCA) on the multi-modality linear features learning and multi-layer belief networks on multi-modality nonlinear features learning. In order to complete a better result of correlation analysis and classification, the output nodes of multi-layer belief network are used for back propagation (BP) network training. Previous works on solving canonical correlation analysis (CCA) had proposed several models based on deep neural network or regularization, typically involving either some form of norm or auto-encoders with a reconstruction objective. Many existing advanced models had been developed to find the maximal correlation in multi-modality data. However, these multi-modality data tend to have the number of feature dimensions which more than that of samples. Differ from these advanced models, our proposed model is applied to analyze the real set of multi-modality data and test several previous models, then comparing them experimentally on fMRI imaging and SNPs genomics. In experiments, the results show that our model, deep principal correlated auto-encoders (DPCAE), learns features with effectively higher correlation and better performance of classification than those previous models. In terms of classification accuracy, the classification accuracy of the datasets exceeds 90%, but that of the CCA-based model are about 65%, and that of the DNN-based model are about 80%, the classification accuracy of the DPCAE is significantly improved obviously. In the experiment of clustering performance evaluation, the DPCAE further verified its superior classification performance with an average normalized mutual information index of 93.75% and an average classification error rate index of 3.8%. In terms of maximal correlation analysis, the model outperforms other advanced models with a maximal correlation of 0.926, showing excellent performance in high-dimensional data analysis.
Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach

Automatic cruise control of a platoon of multiple connected vehicles in an automated highway system has drawn significant attention of the control practitioners over the past two decades due to its ability to reduce traffic congestion problems, improve traffic throughput and enhance safety of highway traffic. This paper proposes a two-layer distributed control scheme to maintain the string stability of a heterogeneous and connected vehicle platoon moving in one dimension with constant spacing policy assuming constant velocity of the lead vehicle. A feedback linearization tool is applied first to transform the nonlinear vehicle dynamics into a linear heterogeneous state-space model and then a distributed adaptive control protocol has been designed to keep equal inter-vehicular spacing between any consecutive vehicles while maintaining a desired longitudinal velocity of the entire platoon. The proposed scheme utilizes only the neighbouring state information (i.e. relative distance, velocity and acceleration) and the leader is not required to communicate with each and every one of the following vehicles directly since the interaction topology of the vehicle platoon is designed to have a spanning tree rooted at the leader. Simulation results demonstrated the effectiveness of the proposed platoon control scheme. Moreover, the practical feasibility of the scheme was validated by hardware experiments with real robots.
Behaviour Analysis of Multilayer and Bulk Heterojunction Organic Solar Cell With Aluminium-Doped Zinc Oxide Anode Layer

The production of electricity by harnessing the energy of the sun is done at almost no pollution and at very low cost. Due to the economical and ecological advantages, organic solar cells are evolved. This paper presents the affect on power conversion efficiency (PCE) by adding an active layer in a bulk heterojunction (BHJ) organic solar cell along-with one thin intermediate layer. Firstly, the efficiency is demonstrated by single layer BHJ with P3HT:PCBM material as an active layer. Furthermore, one additional active layer of MEH-PPV:PCBM material is added in BHJ with former material, an intermediate layer of organic material hexdecafluoro copper phthalocyanine (F16CuPc) is also introduced and the affect on efficiency is studied using Silvaco Atlas TCAD. Also, aluminium-doped-zinc-oxide (AZO) material which is a transparent-conducting-oxide (TCO) is used as anode layer. The Power-Conversion-Efficiency (PCE) of a solar cell depends on short-circuit-current-density (Jsc), open-circuit-voltage (Voc) and fill_factor (FF). The increment of the short circuit current is observed in multilayer in comparison to BHJ. Therefore, PCE is increased from 7.4% to 10.3%. Hence, the addition of active layer in BHJ increases the PCE of organic solar cell or photovoltaic cell (OPV).
Monetization of Services Provided by Public Fog Nodes Using Blockchain and Smart Contracts

Public fog nodes can be deployed in public places closer to the edge where many personal and commercial devices (e.g., a sensor, an application, or a device) can connect to. These public fog nodes can provide real-time and localized services for networking, computing, storage and content delivery to the connected devices. The monetization and payment of such services is typically manual, centralized, and lacks the necessary trust. The providers of the public fog nodes typically offer fixed pricing models for their services, and the customers manually select and pay for the used services, with little or no transparency and trust in the provided service in terms of the used time, network bandwidth, and quality of service (QoS). This paper presents a novel scheme to enable blockchain-based monetization and automated payment in cryptocurrency for services provided by public fog nodes. The proposed scheme is decentralized, trustworthy, automated, and with certain guarantees for QoS, customer satisfaction, and dispute resolutions through a reputation system. The proposed solution uses the Ethereum blockchain and its native smart contract features to govern the interactions between devices and fog nodes. The proposed solution is implemented, tested and evaluated to show correct behavior and functionality. We also provide cost and security analysis and show that our solution is resilient against major security attacks. Our smart contract is made publicly available on Github1.1https://github.com/mazendb/fognodemonetization
Devops, A New Approach To Cloud Development & Testing

Organization’s proficiency to deliver services and applications at high velocity requires competing effectively in the market. The practices and tools for such management processes demands the quick and reliable model. Changes need to start at the software engineering level when building applications in the cloud, so there is a need to automate our DevOps processes using cloud and non-cloud DevOps automation tools. The aim of this paper is to move DevOps to Cloud and become more agile at software development and operations. In parallel, consideration of how to extend those DevOps process and automation into public and/or private clouds are the prime approach for this research. While investigating the emergence of DevOps, this paper paints it as a dramatic change in IT world for process improvement. Goal is to understand how the DevOps and Cloud work together to help businesses achieve their transformation goals.
Game Engine PBR for Background CGI Production of Live-action Contents

Real-time physically-based rendering of a game engine is emerging as a way to increase 3D CGI production efficiency of video contents. However, it is qualitatively different from existing 3D rendering software due to the algorithm for real-time processing of physically based rendering. In this paper, we are to propose a method of using game engines for the production of live-action contents based on the structural similarity between rendering results of 3D rendering software and game engines. The rendering results of the 3D rendering software and the game engine showed the structural similarity of high figures, but there were some differences in the shadows and reflections due to the differences in the processing of light. Therefore, game engines can be used for background production after middle distance, where structural forms are important, not shadows and reflections.
Research on Adaptive Monte Carlo Location Method Based on Fusion Posture Estimation

In the process of global positioning of robots, the errors of odometer caused by the abduction of robots and the errors of laser data matching results caused by ambient characteristics will lead to inaccurate Monte Carlo positioning. In this paper, an adaptive Monte Carlo localization algorithm based on position and attitude estimation is proposed. The real-time position and attitude are estimated by matching the scanning data of the laser sensor, and the real-time location is realized by combining the adaptive Monte Carlo algorithm. The results of simulation experiments on ROS show that the algorithm is superior to Monte Carlo algorithm in particle concentration and positioning accuracy, and has good robustness to the abduction problem of robots and the environment without obvious characteristics, which solves the positioning problem caused by the uncertainty of odometer.
EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial Odometry

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods.