Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Autoencoder Approach to Received Signal Strength-Based Localization with Unknown Channel Parameters

We propose a new framework for the received signal strength-based localization when channel parameters are unknown. The proposed positioning method consists of two parts: the offline phase and the online phase. In the offline phase, we learn the relation between the RSS and the channel parameters using a deep autoencoder structure. By exploiting the estimated parameters from the autoencoder, we determine the user's location based on the least-squares solution. The simulation results show that the accuracy of positioning can be effectively achieved with the proposed scheme even though the channel parameters are unknown.
A Robust Polarmetric SAR Terrain Classification Based on Sparse Deep Autoencoder Model Combined With Wavelet Kernel-Based Classifier

Since the existing terrain classification algorithm based on deep learning is not ideal for unbalanced PolSAR classification, a effective terrain classification algorithm based on wavelet kernel sparse deep coding network under unbalanced data set is proposed in this paper. The algorithm firstly adopts a structured sparse operation so as to enhance the accuracy of feature propagation and reduce the amount of stored data, where the unimportant parameter connections in each group are gradually reduced by dividing the network convolution kernel into multiple groups during the training process; The wavelet kernel-based classifier is used instead of the Sigmoid function to classify and identify features for different terrain, which has high generalization performance for small sample, nonlinear and high-dimensional mode classification problems. The experimental results show that our proposed classification algorithm can improve the classification performance of unbalanced samples, and improve the classification efficiency while ensuring the accuracy of classification.
Modeling and Experimental Study of a Novel Multi-DOF Parallel Soft Robot

In view of the demand for flexible drive and large load of the soft robot in the practical application, a novel type of flexible-actuated multi-degree-of-freedom (multi-DOF) parallel soft robot is designed. The proposed robot in parallel combination of three groups of flexible-actuated elements (FAEs) realizes large load by increasing the bearing area at the connection between flexible-actuated units (FAUs). In order to improve the driving flexibility, the multi-layer FAU is used to drive independently in parallel so as to realize omnidirectional bending movement by pneumatic drive. With the coupled analysis in terms of motion and force, the mapping model of kinematic attitude parameters and the external load force with output air pressure value is established. Finally, an experimental prototype is developed and an experimental test platform is built. Then, the comparison among the experimental data, simulation results and theoretical results verifies the capability of multi-DOF omnidirectional movement and flexible-actuated large load.
Germination Prediction of Sugar Beet Seeds Based on HSI and SVM-RBF

Beet is an important sugar crop in China. The selection of beet seeds is a key link in the process of agricultural breeding. Hyperspectral technology has the advantages of fast, real-time, accurate and lossless acquisition of seed morphological characteristics, internal structural characteristics, chemical composition and other characteristic information, and has a good application prospect in seed quality testing, classification and identification. In this study, the near infrared hyperspectral image acquisition system was used to obtain the hyperspectral images of 3072 samples. The average spectrum of seed area was extracted as its characteristic spectrum. Ten characteristic wavelengths of characteristic spectrum were selected by continuous projection algorithm, and then the model was established by SVM-RBF algorithm. The model accuracy of this test device is 87.3%. The results show that high spectral imaging can predict the germination of beet seeds accurately, which provides a new idea for online nondestructive testing of beet seeds.
A New Simulated Database for Classification Comparison in Power Signature Analysis

The selection of the most appropriate detection, feature extraction and classification method is a fundamental step for the Non-Intrusive Load Monitoring (NILM) problem. In order to compare methods, a properly identified and annotated dataset is required. In this sense, several datasets have been proposed in the literature, real and simulated, with different features, loads and acquisition scenarios. In general, a common characteristic of these datasets is the absence of multiple simultaneous loads with a balance between the loads that are switched, precise indication of load events, and inclusion of noise and harmonic content. Such limitations may comprise a proper comparison between load disaggregation methods, hindering subsequent tasks, such as embedding the solution in electronic systems. With the aim of including all these requirements, this work presents a new simulated dataset using MATLAB-Simulink models, validated with real data, that controls the instant that each load is switched, allowing to precisely extract features during the transient of each load. Additionally, by varying the parameters of the simulation such as harmonic content and noise, it is possible to evaluate the performance of state-of-the-art methods (Voltage-Current Trajectories, Discrete Fourier and Wavelet Transforms) for load classification. In general, Voltage-Current Trajectory is the most affected method in low signal-to-noise ratio condition.
Visualizing the Malicious of Your Network Traffic by Explained Deep Learning

In recent years, insider threats within computers have been overgrowing because a high quantity of malware and its variants have been spread massively by spam mail, malvertising attack, and users' carelessness. Moreover, some of the dormant malware would not be inspected by ant-virus software, and the risk exists continually until finally becoming a disastrous economic loss. Several studies developed signature-based methods to detect insider threats, but we are more interested in how to simplify the network behavior from sophisticated traffic flow. Without inspecting network payload and packet with time-consuming, we focus on the traffic behavior that we only consider the features of source IP, destination IP, timestamp of connection, and quantity of connection. To conquer the black box of complicated network traffic, this work applies the deep learning paradigm and proposes the variant version of VGG16 to examine the features within traffic flow. Finally, this paper proposes a method to support more explanation on traffic behavior with learning model.
A Quadral-Fuzzy Control Approach to Flight Formation by a Fleet of Unmanned Aerial Vehicles

This paper addresses the control of a fleet of unmanned aerial systems (UAVs), termed as drones, for flight formation problems. Getting drones to fly in formation is a relevant problem to be solved when cooperative cargo transportation is desired. A general approach for this problem considers the coordination of a fleet of UAVs, by fusing all information coming from several individual sensors posed on each UAVs. However, this approach induces a high cost as every UAV should have its advanced perception system. As an alternative, this paper proposes the use of a single perception system by a fleet composed of several elementary drones (workers) with primitive low-cost sensors and a leader drone carrying a 3D perception source. We propose a Quadral-Fuzzy approach to ensure that all drones fly in formation and will not collide with each other or with environment obstacles. We also develop a new way to compute potential fields based on possibility fuzzy (fuzziness) measure with the focus of avoiding collisions between the drones. The proposed approach encompasses four high-coupled intelligent controllers that respectively control the leader and worker drones’ motion and implement obstacle and collision avoidance procedures. Simulation results using a fleet of four aerial drones are presented, showing the potential for solving usual problems to flights in formation, such as dodging obstacles, avoiding collisions between the drones, among others.
Analysing Fuzzy Based Approach for Extractive Text Summarization

In today’s era of information, there is gigantic amount of data available from various sources. Not only does the enormous volumes pose problems, searching of required information becomes a very difficult task. It is the need of the hour to have smaller but significant representation of large, bulky pieces of information in order to obtain the desired details. Text summarization is the process of condensing text documents into shorter and accurate representation conveying the meaning of the text precisely. It has found applications in numerous fields. In this paper, a process for extractive text summarization using fuzzy logic has been discussed meticulously. It takes various properties into account for identifying the most significant sentences for the formation of summary from a given text. The model proposed in this paper has been tested on BBC News Summary dataset and the results have been compared using the ROUGE measures. The obtained results indicate that the proposed model shows enhanced performance with improved f-measure values.
Safeguarding Intelligent Decision-Making for Business : Towards A Model

The growing use of Artificial Intelligence (AI) in the business world and its effects continue to dominate debates given their penetration in the organizational processes including business decision-making. This article scrutinizes the application of AI technologies in the organizational processes and the security inferences for such establishments with a particular focus on decision-making processes within the business context. The importance of this study is that it provides a new procedural design that deliberates the safety implications of organizational decision-making aided by the AI systems. The analysis is based on a combination of literature on organization decision making and AI technology security concerns. Additionally, this mirrors the need to empirically test this novel model using different case studies.
Keeping Host Sanity for Security of the SCADA Systems

Cyber attacks targeting the Supervisory Control and Data Acquisition (SCADA) systems are becoming more complex and more intelligent. Currently proposed security measures for the SCADA systems come under three categories: physical/logical network separation, communication message security, and security monitoring. However, the recent malwares which were used successfully to disrupt the critical systems show that these security strategies are necessary, but not sufficient to defend these malwares. The malware attacks on the SCADA system exploit weaknesses of host system software environment and take over the control of host processes in the SCADA system. In this paper, we explain how the malware interferes in the important process logics, and invades the SCADA host process by using Dynamic Link Library (DLL) Injection. As a security measure, we propose an algorithm to block DLL Injection efficiently, and show its effectiveness of defending real world malwares using DLL Injection technique by implementing as a library and testing against several DLL Injection scenarios. It is expected that this approach can prevent all the hosts in the SCADA system from being taken over by this kind of malicious attacks, consequently keeping its sanity all the time.
Modeling and Control of PMSG Based Wind Energy Generation System

In the past few years, a rapid increase in the cost of the conventional form of energy, it also acts as a major contributor in environmental pollution and environment damage. Rapid and overuse of this form of energy has led to scarcity in resources, thus there is an urge for the utilization of the renewable form of energy. This research work deals with the power generation via wind energy with gear box connected between generator and the PMSG, so as to provide rated torque to the generator. The following model is implemented using Matlab/Simulink file. A control loop using PI control has been implemented so as to get and perfect optimized output which is then feed to the grid. This paper also discusses various steps taken to get the desired output
An Efficient Algorithm for an ℓ₁/ℓ₂ Mixed Optimal Control Problem with a Box Constraint and Parallelization

An <inline-formula> <tex-math notation="LaTeX">$\ell _{1}/\ell _{2}$ </tex-math></inline-formula> mixed optimal control problem for discrete linear systems is considered in this letter. Compared to conventional LQ-type optimal control problems, which consists of quadratic costs on states and inputs, an <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> cost on the input are included to induce the sparsity of the control input sequence. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is proposed. In the proposed algorithm, proximal points are computed by the closed form solution. Furthermore, by exploiting the structure of the optimal control cost function, the proposed algorithm is parallelized. Finally, numerical examples illustrate the effectiveness of proposed algorithms.
Recommending Sharing Rides based on A Binary Differential Evolution Algorithm

Ridesharing is a transportation model based on shared use of cars to reduce the number of cars in use, fuel consumption, air pollution, parking problem and congestion. To realize ridesharing, the key issue is to develop an algorithm to recommend ridesharing information to passengers and drivers. In this paper, we propose a solution methodology to provide ridesharing information for drivers' and passengers' by developing a Differential Evolution (DE) algorithm with three different binary search strategies to generate trial solutions. The solution algorithm has been implemented and several experiments have been conducted to compare the effectiveness of the three search strategies.
Wiga: A WiFi-Based Contactless Activity Sequence Recognition System Based on Deep Learning

Monitoring aperiodic activity sequence contributes a lot to home exercise guidance and sports experience but existing approaches are designed for quasi-period activity or isolated activity monitoring. There is a compelling need for contactless real-time auxiliary exercise system, especially for aperiodic activity sequence. In this paper, we present Wiga, a WiFi-based real-time contactless activity sequence recognition system, which can recognize activity sequences even for users who have not participated in the training phase. Wiga takes the fine-grained Channel State Information (CSI) as input and elaborates a deep learning network to map the motion-induced signal variations with the activity sequence. First, Wiga removes noise and redundancy of the raw CSI measurements. Then, after abstracting deep features with a Convolutional Neural Network (CNN), Wiga exploits a Long Short Term Memory (LSTM) network to model temporal dependencies of the sequence. In addition, Wiga employs the beam search method to get around error-prone temporal segments and obtains real-time activity sequence recognition. We evaluate Wiga with 17 yoga activities from 7 volunteers, and extensive experimental results show that Wiga achieves an average accuracy of 97.7% and 85.6% for trained and untrained users respectively with a recognition delay no more than 0.5s.
Vehicle Accident and Traffic Classification Using Deep Convolutional Neural Networks

Road Traffic accident is a horrifying and terrifying phenomenon which is the main cause of death of more than 1.35 million people and injuries to over 50 million people per year. The global economic loss caused by road traffic accidents annually is estimated at 518 billion US dollars. In this work, we investigate the categorization of traffic and accident images according to their content, features, and semantics using deep learning techniques. The availability of appropriate data representation enhances the performance of deep neural networks in feature extraction using supervised domain knowledge of the data to create valuable pattern understanding. In this work, we have gone through the classification of images using CNN techniques and categorize images into four predefined classes i.e. Accident, Dense Traffic, Fire, and Sparse Traffic. Our deep Convolutional Neural Network (CNN) model learning the mapping of the input images to their labeled classes and show good generalization to the test dataset. Experimental results on real-world datasets have shown that the proposed CNN method is effective for accident and traffic classification, and can perform the task with an accuracy of 94.4% on four target traffic and accident classes.
Performance Evaluation of Application Mapping Approaches for Network-on-Chip Designs

Network-on-chip (NoC) is evolving as a better substitute for incorporating a large number of cores on a single system-on-chip (SoC). The dependency on multi-core systems to accomplish the high-performance constraints of composite embedded applications is on the rise. This leads to the realization of efficient mapping approaches for such complex applications. The significance of efficient application mapping approaches has increased ever since the embedded applications have become more complex and performance-oriented. This paper presents the detailed comparative analysis and categorization of application mapping approaches with current trends in NoC design implementation. These approaches target to improve the performance of the whole system by optimizing communication cost, energy, power consumption, and latency. Apart from the categorization of the discussed approaches, comparison of communication cost, power, energy, and latency of the NoC system carried out on real applications like VOPD and MPEG4. Moreover, the best technique identified in each category based on the evaluation of performance results.
Application of GAN in Semi-Supervised Learning

The third wave of artificial intelligence are inseparable with deep learning. The success of deep learning is mainly attributed to three major factors – big data, big models and big calculations, but they also brought some constraints to the further development and popularization of deep learning. The first challenge is labeling data is expensive, we should design a new learning method that can learn from unlabeled data. Ian Goodfellow who is inventor of GAN, mentioned that the method by which GAN can be used for semi supervised learning is called SSGAN. The experimental results show that the discriminant model trained by this processing method has better effects than other methods in rationally using unlabeled data.
Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects

Integrated circuit chip fabrication may induce defects on silicon wafers due to inadequate manufacturing environment, equipment malfunctioning and operational flaws. An identification and analysis of these defects facilitates the process engineering by backtracking and addressing their causes of generation. There exist various computer vision techniques to automate the silicon wafer defect inspection. Recently, deep learning has been employed for the task, with its diverse modeling approaches and network configurations, aiming at providing the best performing classifiers for wafer defect inspection. However, the data for wafer inspection is known to be highly imbalanced. For instance, WM-811K, having abundance of non-defective images and small sets of samples in defect classes. Deep networks trained on such uneven data tend to bias towards major classes leading to false classifiers. This research proposes a convolutional neural network while addressing class imbalance through data undersampling. The proposed method performance has been evaluated by a set of metrics and results are compared with an existing machine learning approach. The results comparison demonstrated 90.44% test accuracy of the method as compared to 78.48% of the existing work. This shows that the imbalance mitigation with the proposed strategy offers a better solution for the wafer defects classification.
Modeling Battery Energy Storage System operating in DC microgrid with DAB converter

This paper presents a computational model for studying the energy flow at the integration of a battery bank (BB) to a DC microgrid, through the use of the Dual Active Bridge (DAB) converter. The one time constant model was adopted for the battery, with its parameters obtained through exponential regression by the Least Squares method, through its hysteresis curve between terminal voltage and load level percentage. The curve was synthesized by data collected from a LiFeYPO4 reference battery. The DAB converter was controlled by the Phase-Shift modulation technique. The converter power management occurs through a state machine, which defines the conditions of energy flow between the microgrid and the energy storage system. This paper has educational aims for the study of energy flow at the integration of BB into DC microgrids.
Navigation in Complex Networks Using Random Walk Theory and Principal Component Analysis

With quickly increasing size of complex network, the traditional shortest path routing strongly restricts the ability and scalability of navigation due to the requirement of global network topology. To overcome the drawbacks, we assume a hidden metric space for embedding complex network via random walk theory and principal component analysis. Then, based on the resulting hidden metric space, the distances between pairs of nodes are computed to navigate messages from source to target nodes according to the greedy routing. Because the greedy routing only demands the local network topology, the scalability of navigation is reserved. Furthermore, based on the evaluation metrics, i.e., successful routing rate, hop length, and stretch, we show the greed routing based on network embedding enables the better ability of navigation in both synthetic and real networks.
Joint Allocation and Operational Management of DG and BESS in Distribution System in Presence of Demand Response

This paper aims to study joint allocation and operational management of wind based distributed generation and battery energy storage system (BESS) in distribution system considering demand response to optimize network losses. The optimization model is formulated as a mixed integer second order conic programming and solved using MOSEK solver. The planning framework is assessed on 33 bus distribution system under different case studies. The simulation results show that application of demand response reduces the size of BESS and network energy losses.