Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Analysis for the Classification Purpose of Intrusion Detection System with K-Nearest Neighbors and Deep Neural Network

Nowadays, along with network development, due to the threats of unknown sources, information communication is more vulnerable and require more secured information. An Intrusion Detection System (IDS) is important for protecting information with growing of unauthorized activities in-network. Traditional firewall techniques are less capable to protect information against new intrusion. Numerous researches on intrusion detection system have been conducted but old dataset like Kddcup'99 is analyzed. Problem identified that lack of accuracy to detect intrusion with the current available intrusion system. Hence this study aims to anomaly analysis for the classification purpose of the intrusion detection system with the most update dataset named CICIDS-2017 which can be used for the intrusion detection evaluation. This research has conducted the anomaly analysis for the classification purpose based on the K-Nearest Neighbors (KNN) for the machine learning (ML) and Deep Neural Network (DNN) using the Deep Learning (DL) method. One of the results presents a classification performance based on Matthews Correlation Coefficient (MCC) for ML and DL. DNN has performed significantly higher correctness classifier which shows DNN score 0.9293% compared to KNN is at 0.8824%. This research is significant as reference for IDS development which would improve security response for networked systems.
Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

The indoor location technique plays a essential role during the application of quadrotor unmanned aerial vehicle (UAV). However, the control design problem for the quadrotor UAV is quite difficult in the indoor environment due to the weak GPS signal. Based on Ultra Wide Band (UWB), the related positioning issues can be solved of UAV through base station with known coordinate position and equipment with location tag, but it is difficult to meet the high-precision operation requirements. In this paper, an indoor positioning design method combined with the Inertial Measurement Unit (IMU) and UWB positioning technology is proposed, which can effectively suppress the error accumulation of the IMU and further improve the positioning accuracy. Moreover, the system architecture for a class of quadrotor UAV is designed. The multisensor fusion technology based on unscented Kalman filter (UKF) is used to avoid neglecting the high-order terms of the nonlinear observation equations of UWB and IMU, which can effectively improve the accuracy of solving the nonlinear equations. Finally, a hardware-in-the-loop simulation platform is designed to verify the effectiveness of the indoor positioning method and improve the positioning accuracy.
Contact Dynamic Modeling and Control for Tethered Space Net Robot with Asymmetric Net Configuration

As an improved version of tether-net system, Tethered Space Net Robot (TSNR) shows more advantages on active space debris removal. However, the contact dynamics of TSNR and target are still not well understood. Moreover, considering the truth that the symmetric net configuration is hard to achieve, an asymmetric net configuration is studied in this paper. Based on the contact dynamic model of TSNR, the simulations show that the net cannot close to the minimum naturally. In order to successfully capture the target with the asymmetric net configuration, a sliding mode control scheme is designed to close the opening area of the net to the minimum after collision. The simulation results show that the proposed control algorithm can make the net wrap around the target successfully.
The Role of Individual Edges in Edge Controllability of Complex Networks

Dynamical processes occurring on edges of complex networks are relevant to many real situations. Controlling the edge dynamics is a fundamental challenge in network science. Inspired by recent advances in the edge controllability theories, we explore the role of individual edges in the edge controllability by classifying each edge into one of three categories: critical, redundant, and intermittent. An analytical framework is developed to identify the category of each edge, leading to the discovery that the proportions of three types of edges are to a great extent encoded by the degree distribution, and are affected by the in- and out-degree correlation. In addition, we propose the probability distribution of intermittent edges, and find that the probability distribution has multimodality, which is common in model and real networks.
An Unsupervised Neural Network Approach for Inverse Kinematics Solution of Manipulator following Kalman Filter based Trajectory

A novel unsupervised approach for inverse kinematics solution of a manipulator using artificial neural network is presented. Forward kinematics equations determine the motion of manipulator's arm and have a unique solution. But there is not a unique solution for inverse kinematics as manipulator may have more than one configurations to reach a particular point. Here in this paper, we have taken a PUMA 560 robot with six degrees of freedom with aim to grab an object moving in circular path in XY plane with a known constant height and kalman filter has been used to determine accurate position of that object. Contrary to supervised learning approach, which needs a huge amount of data to train the system, we have used a real time unsupervised approach to solve inverse kinematics problem which is more efficient. Joint angles of the robot are determined in real time using unsupervised feed forward neural network with backpropagation training algorithm.
Towards a synthetic load generator framework for validating schedules in HPC environment

Most of the scientific parallel and distributed applications require significant amount of time to complete on huge and expensive computing systems. Often, these applications are executed multiple times, making their scheduling a critical process for the runtime performance. One way to validate effectiveness of a schedule is to run the application on the actual hardware, which takes a lot of time and resources and is largely unfeasible. In this work we present a baseline for an adjustable-scale graph-based parallel workload executor framework for executing custom-scaled parallel applications given as graphs using synthetic load for the tasks, the GRAPPLER. The idea behind GRAPPLER is to operate as a schedule validator whose execution traces can provide valuable insights into runtime bottlenecks of the application, platform and the scheduler.
FLoc: Fingerprint-Based Indoor Localization System under a Federated Learning Updating Framework

Fingerprinting-based indoor localization via WiFi has achieved a great breakthrough in the past decade. However, it suffers from an inherent problem that the localization accuracy declines sharply over time due to the dynamic environment and unstable WiFi devices. Researchers have designed many methods to update the localization model, e.g., crowdsourcing-based model updating, in order to maintain the localization accuracy. Unfortunately, they have not taken the privacy into consideration during the updating process. This will lead to a threat that the eavesdroppers could guess the location providers' private information according to the updating model. For the goal of maintaining the localization accuracy without the risk of privacy breaching, we proposed FLoc, a fingerprinting-based indoor localization system which updates the localization model via a federated learning framework. In FLoc, every provider maintains a local localization model in their own device. They will regularly encrypt the updating parameters and share them to a common model server. At the model server, it aggregates the encrypted information of the local models to maintain a general model, which will be sent back to the local devices for next updating iteration. We evaluate FLoc in an APs unknown laboratory corridor. The experiment results show that FLoc has a comparable localization performance. Moreover, it can successfully protect the providers' privacy, since the information transferred is all encrypted.
CSDG MOSFET Based Linear Voltage Regulator: A Mixed Signal Device Perspective

Various circuitries that make up an electronic communication system requires different voltage signal to power them, and this comes from voltage sources that are higher than the required. A voltage regulator is required to do the conversion and maintain that stable output signal. However, some system requires different voltages for its different outputs. Due to this need, the design of a voltage regulator with different voltage inputs and multiple output becomes imperative. In this research work, the Cylindrical Surrounding Double-Gate (CSDG) MOSFET has been employed to design linear voltage regulator and its efficiency has been verified. This design proves to have high efficiency, utilizes small device area, and reduces the degrading of devices due to constant system reset caused by feedback.
Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition

Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is among the applications of computer vision that heavily relied on hand crafted features in the past years. LBP Three Orthogonal Planes (LBP_TOP) is one of the most used hand crafted features extractor in the scientific literature to tackle the problem of ME classification. In this paper we present a time unification method that provides better results than the classical LBP_TOP while also drastically reducing the calculations required for feature extraction.
Control of variable speed wind energy conversion systems by fuzzy logic and conventional P&O

This paper presents a modeling and a simulation of a wind energy conversion system, based on a synchronous permanent magnet generator (PMSG), with the integration of maximum power point tracking techniques in order to extract maximum power from the turbine independently of the change in wind speed. The system is controlled by two of the most popular MPPT algorithms: as traditional one perturb and observe (P&O) and the fuzzy logic (FL). The considered model as well the control strategies has been implemented in MATLAB/Simulink environment. Therefore, the presented simulations results show the performance of fuzzy logic (FL) against the conventional P&O.
Inexact Addition and Subtraction for the Reconfigurable FIR Filter Implementation using CSD based Common Subexpression Elimination

Inexact computing may be employed to reduce power and area in applications where, a small error in computations, does not affect the performance of the system. This paper proposes inexact half-subtractor and full-subtractor circuits, in which, the error occurs only in 25% of all the input combinations. The area and power of the proposed inexact half-subtractor are found to reduce by 25% and 39.37% respectively, with the error in difference occurring only in one input combination out of four. The area and power of the proposed full-subtractor are reduced by 50% and 71.53% respectively with the error in difference occur only at two input combinations out of eight. The proposed inexact subtractors and adders are used for the implementation of reconfigurable FIR filters using vertical horizontal common subexpression elimination (VHCSE) in the canonical signed digit (CSD) space. The area, power, and cell utilization are analyzed and found to reduce compared to the existing CSD based VHCSE algorithm. The performance of the proposed FIR filter is also analyzed in terms of pass-band ripple and stop-band attenuation and found to be within the tolerable limits.
Wind farm response on short circuits and longitudinal asymmetries

The steady increase in electricity demand has led to more installed generation capacity in wind farms, which, due to the stochastic nature of their production, may have characteristic and non-standard responses to some occurrences in the grid. The power output from the wind farm changes constantly, depending on the wind speed, so the impact of the network on the wind farm will inversely vary depending on the moment of the short circuit and the wind farm operating mode, since the values of currents and voltages in these fault systems depend on the power flows. Although wind power is known to contribute to the short circuit current / power at the point of its connection, this paper focuses on the response of the wind farm to short circuits in the network and the impact of these phenomena on the grid-wind relation. The aim of the paper is to determine the worst type of short circuit for wind turbine operation in the distribution system.
Stability Analysis of Rod-Driven Continuum Robots Based on Finite Element Models to Avoid Buckling

Continuum robots driven by rods have intrinsic compliance while keeping relatively high motion precision, thus they have promising potential to various fields where compliant actuation and manipulation are required. However, the use of slender rods in the robot structure may lead to buckling problem. In this paper, finite element models were established to analyze the stability of a type of rod-driven continuum robot. Simulations were conducted to find the critical and optimal slenderness ratio used to direct the design of such kind of robots. A prototype was built based on the optimal slenderness ratio and the experimental results proved the accuracy and reliability of the finite element models.
Mota: Multi-Stage Multi-Task Online Assignment Algorithm Based on Opportunistic Crowdsensing

Mobile crowdsensing is a new paradigm, which refers to mobile users exploiting smart devices to conduct complex tasks. Opportunistic crowdsensing derives from mobile crowd sensing In this paper, we focus on task assignment problem in opportunistic crowdsensing, in which the mobility model of users is predictable. In order to minimize the average makespan of all tasks, we propose a Multi-stage multi-task Online Task Assignment (MOTA) algorithm. The algorithm adopts greedy strategy and multi-stage assignment strategy for multi-task assignment problem. Simulation experiments show that the average makespan of all tasks can be reduced by 10% to 15% on average.
Reliability Evaluation of Compressed Deep Learning Models

Neural networks are becoming deeper and more complex, making it harder to store and process such applications on systems with limited resources. Model pruning and data quantization are two effective ways to simplify the necessary hardware by compressing the network with relevant-only nodes and reducing the required data precision. Such optimizations, however, might come at a cost of reliability since critical nodes are now more exposed to faults and the network is more sensitive to small changes. In this work, we present an extensive empirical investigation of transient faults on compressed deep convolutional neural networks (CNNs). We evaluate the impact of a single bit flip over three CNN models with different sparsity configurations and integer-only quantizations. We show that pruning can increase the resilience of the system by 9x when compared to the dense model. Quantization can outperform the 32-bit floating-point baseline by adding 27.4x more resilience to the overall network and up to 108.7x when combined with pruning. This makes model compression an effective way to provide resilience to deep learning workloads during inference, mitigating the need for explicit error correction hardware.
An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew’s correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.
Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods

Nowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources.
A Fabrication Device Producing Twisted and Coiled Polymer Actuators for Use in Soft Robots

The twisted and coiled polymer actuator is a kind of newly discovered high-performance artificial muscle, among which the elongating type has great potential to be applied into soft robots. However, to fabricate elongating nylon muscles, a mandrel is necessary during the coiling process, resulting in more difficulty. To this end, we introduce a fabrication device which is capable of producing contracting or elongating actuators with different minor diameters and controlling the actuators' pitch. Therefore, the homogeneity and uniformity of the actuators are guaranteed. The fabricated actuator can provide a stroke up to 50% or a force up to 1N, and shows a good self-sensing characteristic. A simple 1-DOF soft manipulator was made to demonstrate the actuation capability of the produced actuator. The proposed device provides convenience for the fabrication of future soft robots powered by this kind of actuator.
Analysis of Multitasking in Divided Attention using Machine Learning

Evaluation of cognitive functionality plays an important role in the career choice of students as well as for the selection of employee for the employer. Divided attention is one such cognitive ability that deals with allocation of attention to multiple tasks simultaneously. An accurate analysis of divided attention would help us to identify cognitive decline, as well as provides a quantifiable indicator of a salient feature viz., vigilance which is highly relevant for defence personnel as well as pilots in air, water and road. The close observation of divided attention in home or classroom environment is an essential component for early detection of cognitive problems. It also helps in assessing the effectiveness of learning patterns. This work proposes a new method to determine the ability of relative divided attention through unobtrusive monitoring of use of a software game. The process measures the performance of a user (college student) on a multi-task cognitive software by computing the scores as part of the test for divided attention. This metric indicates the user's ability of multitasking in divided attention, i.e whether user is efficiently paying attention to all the tasks at once, or is primarily attending to one task at a time (sacrificing optimal performance). The data set is labelled based on statistical analysis. After classifying the data using machine learning model (random forest), the academic performance of the user is analysed against the divided attention levels to establish a correlation among them.
Design, Prototype, and Control Design Based on Computed Torque Control of Selective Compliance Assembly Robot Arm

This paper presents the design, the prototype and the control of SCARA (Selective Compliance Assembly Robot Arm) to perform pick and place tasks for industrial applications. The robot has four degrees of freedom (DoFs) with a capability to carry payloads up to 1 kg with high accuracy, precision, and repeatability. The robot is designed using the CAD tools and its prototype development is carried out by manufacturing its mechanical parts (links, base) and selecting the proper off the shelf electrical (motors, controllers) and transmission components (belts, pulleys). We also present the mathematical formulation consisting of direct kinematics, inverse kinematics, and the dynamical equations of the robot. We also report a closed loop position control based on the Computed Torque Control (CTC) method. The Numerical simulations are performed in order to evaluate the performance of the robot by using the applied control technique. The future work includes the experiments on the hardware using the implemented control technique.
Application of Model Predictive Control in a Resolver-to-Digital Converter

A resolver is an angular position sensor used in applications that demands robustness and reliability, such as electric vehicles. However, getting the angular position from resolver output signals is a difficult task, and many algorithms were proposed to achieve that task. This paper describes the application of model predictive control (MPC) to get the angular position from resolver signals. Synchronous demodulation is used to get the envelopes of the resolver outputs and get the estimation error. The structure of the conventional model predictive controller was modified to be used as an angle tracking observer (ATO). Simulations show the performance of the proposed approach. According to the bibliographic review of the authors, it is the first time that model predictive control is applied as an observer to get the angular position from resolver signals.