Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Quadratic Optimization for Tracking Problems with Application to a Tray Indexing System

Iterative Feedback Tuning (IFT) algorithm explores the correlation between the cost function and the parameters of the controller. It introduces one data-driven approach to derive the gradient of the cost function with respect to the controller parameters and provides an iterative numerical method for the controller parameters optimization. However, classical IFT algorithms require three experiments to be conducted at one iteration, and one special experiment is included in the three experiments. That means the reference signal is changed during the iteration because of the special experiment, which restricts the application of the IFT algorithm in many areas. Besides, in many process control applications with long duration, IFT algorithm is much more time-consuming than many other tuning algorithms. In this paper, a novel IFT algorithm is proposed. For each iteration, no special experiment is required, which means parameters of the controller can be tuned when the system is running normally. Simulation results on a tray indexing system demonstrate the high performance and applicability of this algorithm.
Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization

The minibatching technique has been extensively adopted to facilitate stochastic first-order methods because of their computational efficiency in parallel computing for large-scale machine learning and data mining. Indeed, increasing the minibatch size decreases the iteration complexity (number of minibatch queries) to converge, resulting in the decrease of the running time by processing a minibatch in parallel. However, this gain is usually saturated for too large minibatch sizes and the total computational complexity (number of access to an example) is deteriorated. Hence, the determination of an appropriate minibatch size which controls the trade-off between the iteration and total computational complexities is important to maximize performance of the method with as few computational resources as possible. In this study, we define the optimal minibatch size as the minimum minibatch size with which there exists a stochastic first-order method that achieves the optimal iteration complexity and we call such a method the optimal minibatch method. Moreover, we show that Katyusha (in: Proceedings of annual ACM SIGACT symposium on theory of computing vol 49, pp 1200–1205, ACM, 2017), DASVRDA (Murata and Suzuki, in: Advances in neural information processing systems vol 30, pp 608–617, 2017), and the proposed method which is a combination of Acc-SVRG (Nitanda, in: Advances in neural information processing systems vol 27, pp 1574–1582, 2014) with APPA (Cotter et al. in: Advances in neural information processing systems vol 27, pp 3059–3067, 2014) are optimal minibatch methods. In experiments, we compare optimal minibatch methods with several competitors on L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}-and L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_2$$\end{document}-regularized logistic regression problems and observe that iteration complexities of optimal minibatch methods linearly decrease as minibatch sizes increase up to reasonable minibatch sizes and finally attain the best iteration complexities. This confirms the computational efficiency of optimal minibatch methods suggested by the theory.
Bilateral Motion Prediction and Tracking Control for Nonlinear Teleoperation System With Time-varying Delays

The motion prediction and tracking control problem for nonlinear teleoperation system with time-varying delays are investigated in this paper. A novel model-independent prediction method with an observer-based structure is proposed. The positions of both slave and master robots are estimated by using delayed measurements through two predictors which are located on the another side of the robot to be measured. It is proven that the prediction errors can converge to zero by applying the Lyapunov method. Predictive controllers of both master and slave robots are designed by applying the prediction results instead of delayed measurements. Some sufficient conditions are derived to design suitable parameters of controllers acording to Lyapunov theory. Finally, the effectiveness of proposed predictor and controller is verified by simulations.
Selection Method of Typical Time Sequential Scenarios Based on Comprehensive Evaluation Index System

High distributed generation integration into power systems increases the randomness and volatility of system operation. The selection of typical time sequential scenarios can effectively improve the speed and accuracy of the operation and planning calculations. However, the existing methods are difficult to select typical representative scenarios from a large amount of data, and lack effective evaluation indexes. Considering the total and distribution characteristics of resources and loads, as well as the influence of common and extreme scenarios on scenario selection, this paper proposes a comprehensive evaluation index system for typical sequential scenarios selection. Then, a multi-objective mixed-integer linear programming (MOMILP) model for typical scenarios selection is established, and a two-stage fuzzy method is used to solve the compromise solution. Finally, the effectiveness and feasibility of the proposed method is verified in an economic dispatch and planning case of a real microgrid.
Effectiveness of Ptex method in the field of Texture Mapping and Polygon Topology

This paper presents several texturing problems that occur in complex polygon topology where the model is based on tris and ngons. It also shows an experiment and describes the application of the Ptex system for mapping textures onto a 3D model. The topology parameterization using 3D Coat and its auto-retopology tools are shown as well. A new topology model was tested using a texture checker inside Mudbox and VRay. Additionally, a comparison was made between Mantra and VRay rendering systems, where specific difficulties had occurred when applying Ptex mapping. The aim of this experiment is to present how cache memory directly interferes with texture filtering and rendering speeds. This paper concludes with the advantages and disadvantages observed in relevant literature and through an empirical approach to this technology.
Determining input variable ranges given a trained regression model and an output range

Industrial process control systems try to keep an output variable within a given tolerance around a target value. PID control systems have been widely used in industry to control input variables in order to reach this goal. However, this kind of Transfer Function based approach cannot be extended to complex processes where input data might be non-numeric, high dimensional, sparse, etc. In such cases, there is still a need for determining the subspace of input data that produces an output within a given range. This paper presents a non-stochastic approximation to determine input values for a mathematical function or trained regression model given an output range. The proposed method creates a synthetic training data set of input combinations with a class label that indicates whether the output is within the given target range or not. Then, a decision tree classifier is used to determine the subspace of input data of interest. This method is more general than a traditional controller as the target range for the output does not have to be centered around a reference value and it can be applied given a regression model of the output variable, which may have categorical variables as inputs and may be high dimensional, sparse. The proposed method is validated with a proof of concept on a real use case where the quality of a lamination factory is established to identify the suitable subspace of production variable values.
Combining Photometric Features and Relative Position to Detect and Track Target Person

Tracking a target person is a vital job in human-robot interaction. The robot must always notice a particular person as the interaction target partner. However, it is sometimes very hard to distinguish the target person because there are many other persons around the target. In this paper, we propose a target person detection and tracking system by combining person’s frontal photometric features such as face and clothing color, and coordinate of the person’s location in the real world. We apply an illumination invariant face recognition method named OptiFuzz. Hue-Saturation histogram (HS-histogram) is used to obtain the clothing color feature, and a location of the person is acquired from a calibrated single camera view. All these features are then fed into an algorithm of Naive Bayes to discriminate between the target person and others. Our experimental results indicate a successful outcome as it is always possible to detect and track the target person.
Test and Measurement of LPWAN and Cellular IoT Networks in a Unified Testbed

One of the main requirements of spatially distributed Internet of Things (IoT) solutions is to have networks with wider coverage to connect many low-power devices. Low-Power Wide-Area Networks (LPWAN) and Cellular IoT(cIOT) networks are promising candidates in this space. LPWAN approaches are based on enhanced physical layer (PHY) implementations to achieve long range such as LoRaWAN, SigFox, MIOTY. Narrowband versions of cellular network offer reduced bandwidth and, simplified node and network management mechanisms, such as Narrow Band IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). Since the underlying use cases come with various requirements it is essential to perform a comparative analysis of competing technologies. This article provides systematic performance measurement and comparison of LPWAN and NB-IoT technologies in a unified testbed, also discusses the necessity of future fifth generation (5G) LPWAN solutions.
Weakly Supervised Temporal Action Localization by Multi-Stage Fusion Network

Most temporal action localization methods are usually trained using video data-sets with frame-wise annotations which are expensive and time-consuming to acquire. To alleviate this problem, many weakly supervised temporal action localization methods which only leverage video-level annotations during training are proposed. In this paper, we first analyze three problems of weakly supervised temporal action localization, namely feature similarity, action completeness, and weak annotation. Based on these three problems, we propose a novel network called multi-stage fusion network, which decomposes the problems into three different modules within the network, namely feature, sub-action, and action modules. Specifically, for feature similarity, a Triplet Loss was introduced to ensure the action instances from the same class having similar feature sequences and expand the margin of the action instance from different classes in the feature module. For action completeness, each stage of the sub-action module can discover the different sub-actions. The complete action instances can be localized in the action module by fusing multiple sub-actions from the sub-action module. To alleviate weak annotation, we localize multiple action proposals from multi-stage outputs of the network in the action module and select the action proposals with higher confidence scores as predicted action instances. Extensive experiment results on data-sets Thumos’14 and ActivityNet1.2 demonstrate that our method outperforms the state-of-the-art methods and the average mean Average Precision (mAP) on Thumos’14 is significantly improved from 40.9% to 43.3%.
Fast mesh denoising with data driven normal filtering using deep autoencoders

Through the years, several works have demonstrated high-quality 3D mesh denoising. Despite the high reconstruction quality, there are still challenges that need to be addressed ranging from variations in configuration parameters to high computational complexity. These drawbacks are crucial especially if the reconstructed models have to be used for quality check, inspection or repair in manufacturing environments where we have to deal with large objects resulting in very dense 3D meshes. Recently, deep learning techniques have shown that are able to automatically learn and find more accurate and reliable results, without the need for setting manually parameters. In this work, motivated by the aforementioned requirements, we propose a fast and reliable denoising method that can be effectively applied for reconstructing very dense noisy 3D models. The proposed method applies conditional variational autoencoders on face normals. Extensive evaluation studies carried out using a variety of 3D models verify that the proposed approach achieves plausible reconstruction outputs, very relative or even better of those proposed by the literature, in considerably faster execution times.
Computational Study of p-n Carbon Nanotube Tunnel Field-Effect Transistor

In this article, a new gate-all-around (GAA) p-n carbon nanotube TFET (CNT-TFET) is proposed and compared with its conventional counterpart. The nonequilibrium Green’s function (NEGF) formalism is used to perform the quantum simulations, where the ballistic transport is assumed. The quantum simulation study includes the transfer characteristics, subthreshold slope, ON–OFF current ratio, switching speed, and power–delay product. The simulations show that the proposed CNT-TFET that has a single junction exhibits better aforementioned figures of merit than the conventional p-i-n device. In addition, the p-n CNT-TFET shows higher current ratio and steeper subthreshold swing than those provided by the conventional design with the gate length downscaling. The obtained results make the proposed nanoscale TFET as a promising device for the future nanoelectronics.
Time-related Constraints in Administration Shell Design within Cyber-physical Production Systems

The concept of Cyber-physical Production System has recently emerged. It emphasizes the expected and very close connection between industrial software systems and production assets on the next generation of intelligent industrial systems. The cyber-physical conceptualization hints at assets making their functions available through the so called administration shells. These are software structural design tenets that from, a functional perspective, will not only incorporate software interfacing aspects but also autonomous behaviour. There is an important body of practice in what is now perceived as cyber-physical integration. However, such work has taken a somehow informal approach to formalizing system engineering aspects and in particular to the impact of time-related constraints affecting, the design locally and, the CPPS as a whole. This paper provides a model that relates physical, logical and computational aspects of cyber-physical design from structural and behavioural perspectives. Its goal is to provide a starting point for designing cyber-physical components while supporting informed decision making in respect to software design, integration and deployment practices that enable these components to operate in cyber-physical synchronization.
The Discourse on Learning Styles in Online Education

Online learning is a contemporary concept in which ideas, models, and traditional teachings have changed. We examine how learning style affects the learner’s academic achievements in online lessons and gender-specific environments. This research investigates whether VARK styles are associated with course results, regardless of the actual VARK results, and whether any study techniques are associated with course results. The results indicate that almost all students did not report on research approaches after their VARK assessment and that the students’ achievement is not associated with their results in each VARK category. This provides additional evidence that the usual teaching styles should be redefined for Online classes where the styles corresponding to the willingness of learners are more difficult to be defined.
Edge Computing for Road Safety Applications

Modern technologies are being developed to address the alarming rise in road accidents caused by drivers’ errors. We leverage computer vision and deep learning at the edge (i.e., in a car) to detect vehicles and pedestrian that are in the surroundings. This information can then be employed to direct driver’s attention to relevant information, minimizing the effects of human errors. This work explores various deep learning pre-trained models: Intel open model zoo and TensorFlow detection model zoo to run inference on Intel Movidius to employ edge computing. We analyze the performance to determine the practicality of using the pre-trained model for road safety purposes. The experiments conducted examine the various Single Shot Multibox Detection (SSD) based models. The accuracy that we obtained by the weighted harmonic mean of the precision and recall on the models, the inference time and low demand in computing power determined that TensorFlow detection model zoo is a practical object detector that we can implement to tackle road safety issue.
The impact of DC/AC ratio on short-term variability of utility-scale PV plants

The growing concern of frequency regulation resulting from high penetration of large-scale Photovoltaic (PV) power plant leads to the need of understanding the characteristics of short-term PV variability and mitigation approach. This study takes the advantage of the trending PV design which is an oversized DC capacity to the AC capacity of the PV plant to study the impact of this oversizing on the short-term variability of PV output. The term, DC/AC ratio is used to define the ratio of the DC capacity to the AC capacity of the plant. Some countering effects are found between the increasing magnitude of variability due to DC capacity expansion and the limit of the AC capacity. Overall, the variability is bounded by increasing the ratio while the impact is more significant during the peak generation period.
A Review of IoT Application in a Smart Traffic Management System

This paper explores the advancement of smart traffic management system using the Internet of Things (IoT). It works as middleware on the foundation of the IoT and augments the idea of the smart city through the traffic light control, smart parking, smart emergent assistance, anti-theft security system, and others. IoT provides an effective way of interactions among the web devices with the traffic embedded sensors, services, actuators, and other interconnected networks. Hence, the application of IoT in the smart traffic management system is not only limited to the reduction of the traffic congestion, air quality improvement, and traffic flow optimization but also extended to the continuous monitoring and ensuring the security and safety for the elderly people. Acquiring multiple sources of traffic information for data analysis, IoT monitors the traffic flow, controls the traffic operation and stores the correct decision for the future information presentation. Having a combination of advanced machine learning approach and data-driven technique, there are implementation limitations of this technology. However, this survey provides a good insight into the application of IoT in the smart traffic management system based on the existing research perspective.
Control of Multilevel Inverter as Shunt Active Power Filter using Maximum Versoria Criterion

This article discusses performance & control of based Shunt Active Power Filter (SAPF) as Multilevel Inverter (MLI)- using DSP of dSpace1104. MLI eliminates the use of bulky and high-cost transformers to connect APF. One of the best alternatives for improving power quality issues is the Cascaded H-bridge multilevel inverter (CHB-MLI) used as shunt active power filter (SAPF). A new controller has been designed based on Maximum Versoria Criterion algorithm for the control of SAPF. The presented control strategy estimates the load currents’ fundamental weight values. Grid reference current and magnitude are calculated using these weights and then the gating pulses for single phase CHB-MLI. The effective output of the presented SAPF control algorithm is illustrated by simulation results using Matlab / Simulink environment & test results are validated in the laboratory on the developed SAPF experimental prototype.
Toward Online Line Switching Methodology for Relieving Power System Overloads

An on-line line switching methodology based on a three-stage solution is proposed to relieve overloads. This novel online methodology, including screening, ranking, and detailed analysis stages, is developed to achieve online application and to provide a set of high-quality switching schemes that can be selected as a preferred solution by system operators. The effectiveness of the proposed online line switching methodology is evaluated on IEEE 39-bus and 118-bus power systems with promising results. The simulation indicates the fast speed and effectiveness of the proposed methodology that make the online application possible.
Load Identification Method Based on Load Feature Extraction and Fuzzy C Mean Clustering Method

Load identification method can identify different types of loads in power systems. Adjustable loads have great potential in providing services to help consume the curtailed wind power. However, the consumption potential of the adjustable loads is difficult to be quantified. This paper makes an in-depth analysis of the necessity of extracting load characteristic parameters, then the characteristic parameters such as maximum adjustable speed, depth of adjustment, load fluctuation coefficient, maximum adjustable time, load time-shift coefficient are proposed. In order to improve the identification precision of adjustable load, the Daubechies fast wavelet transform is used to extract energy value as a new characteristic parameter. On this basis, fuzzy C mean clustering(FCM) is used to identify the load. Finally, through two examples of simulation and method comparisons in a certain area of Hexi, Gansu Province, the proposed identification method is verified that it can effectively improve the identification accuracy and precision of adjustable load.
Quality Risks in the Data Exchange Process for Collaborative CPPS Engineering

The realization of a cyber-physical production system (CPPS) requires suitable methods and tools for the exchange and integration of engineering data between collaborating disciplines. Unfortunately, the description languages used in a CPPS Engineering (CPPSE) organization to describe discipline-specific views are not necessarily well suited for high-quality data exchange between workgroups. In this paper, we identify technical debt and risks regarding CPPSE description languages for data exchange using the VDI 3695 guideline as best practice. We report on effects and likely causes of the quality risks identified in a case study at a large CPPSE company. Based on data from workshops and semi-structured interviews with 28 domain experts from 12 workgroups, we propose a preliminary model relating causes and effects as foundation for analyzing and managing risks in the CPPSE data exchange process.