Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning Based Train Driving Optimization

In contemporary society, railway networks are usually considered among the most critical undertakers of transportation tasks. Thanks to the born-with preferential conditions of the railway systems, in particular the settings and circumstances in which they run, railroad trains are best positioned to become automatic, vis–vis other transportation systems, such as road traffic. Having said that, one must admit that the transformation towards intelligent man-less train driving system will not be easy, given the requirements in high dimensionality, non-linearity, multi-origin restraints, and other factors that may change over time. Challenges, including energy efficiency, on-time performance, and safety, need to be addressed. The authors of the present paper used a deep reinforcement learning (DRL) method to generate energy-optimized train driving solutions. The neural network representation is based on a Bidirectional Long-Short-Term Memory (BLSTM) and an automatic encoder, which integrate sequential properties of the time-series and inherent non-sequential attributes. It is believed that the learned model has value in assisting decision makers in some representative cases of industrial control, as experiments show that this is perfectly adaptable to meet the basic safety, energy saving, and punctuality requirements in train driving.
A Novel Magnetic Induction Communication Transmitter Based on a Mechanical Antenna

The rotating magnet based mechanical antenna (RMBMA) is introduced to realize ELF~VLF magnetic induction communication. The simulation results show that the induced magnetic field generated by the mechanical antenna has little attenuation in seawater and almost no reflection on the air-water interface. It is a promising energy-efficient technology to achieve a longer range magnetic induction communication.
A Novel Ternary Half Adder & One Bit MultiplierCircuits based on Emerging sub-32nm FET Technology

In this paper, we present a novel low-power and high-performance new ternary logic arithmetic circuit that is implemented by double gate (DG) FinFET and graphenenanoribbon (GNR) field effect transistor (GNRFET). Multiple-valued logic (MVL) such as penternary, quaternary and ternary is a promising alternative to the binary logic design, because of less complexity, less computational step and reducedchiparea. The basic ternary gates and its operation are already described in my previous paper [20]. Ternary logic gate based arithmetic combinational circuits such as ternary half adder and one-bitternary multiplier are designed. The proposed ternary combinational circuits are simulated using HSPICE via standard 32nm DG-FinFET and GNRFET technology. Extensive simulation results demonstrate that the Graphene field effect transistor based ternary logic arithmetic circuits are more improved than the DG FinFET technology in terms of power consumption, delay and Power delay product (PDP).
Simplified Floating Offshore Wind Turbine Model for Time-domain Simulation

A development of a simplified floating offshore wind turbine (FOWT) model for time-domain simulation was presented in this paper. We aim to develop a model that can express the most physical properties of the turbine/platform/generator & converter system of the floating offshore wind turbine, limit its complexity, and be suitable for a time-domain simulation program such as PSCAD/EMTDC, MATLAB, etc. The simplified model was developed and implemented using MATLAB, and the model was validated by using Fatigue, Aerodynamic, Structures, and Turbulence (FAST) program. Through this study, the accurate simplified FOWT model for time-domain simulation was achieved and validated.
Evaluation of a Modular Stator, Segmented Rotor Switched Reluctance Motor

The switched reluctance motor (SRM) is a fault tolerant machine that relies on reluctance torque for operation. It can lower machine cost while increasing reliability for applications that most require it. Different structural design modifications that mitigate its disadvantages such as low torque density have been explored, but they are mostly limited to individual investigations of their effects. In this paper, the interaction of these modifications is evaluated in the design of a modular, segmented stator, segmented rotor, switched reluctance motor (SSSR-SRM). This machine is compared to published motor structures with partially conventional designs. Finite element analysis is used to evaluate the machines. The design of a modular motor with independent stator modules and coils is presented. An output torque comparable to SRMs with conventional parts is shown to be possible with appropriate stator and rotor segment sizing.
A Novel Compact UWB Antenna with Radiation Pattern Diversity

In this paper, a novel compact ultra-wideband (UWB) antenna with pattern diversity is proposed. The design consists of two symmetric ultra-wideband elements, which are miniaturized by cutting in half of a original symmetry ultrawideband antenna. The compact two-element antenna array has an overall size of $32\times 32\ \text{mm}^{2}$. Meanwhile, this miniaturization technique applied here can obtain pattern diversity by changing the symmetry of the radiators. A hybrid isolation enhancing technique consisting of an added stub and an inverted-Y slot on the ground ensures the isolation lower than −15dB over the entire UWB spectrum from 3 to 12 GHz. Envelope correlation coefficient and diversity gain are calculated to evaluate the performance of this diversity antenna, and good results are obtained.
Infrared image colorization using an edge aware auto encoder decoder with the multi-resolution fusion

This paper propoes a novel method for near infrared (NIR) image colorization. The proposed method have two parts. In the first part, the RGB output is generated by the edge aware auto encoder decoder, which includes symmetrical jump connections for precise positioning and assistant training networks for enhancing edge and color regions. In the second part, the details of the input NIR image are integrated with the original RGB output, which is a multi-resolution fusion guided by weight maps. No user interaction and similar image is required in our method but also retains clear edges and rich details. We have used a large number of NIR image datasets to evaluate the method in our paper. In the experiments, we show in detail that the proposed method works well and outperforms contemporary state-of-the-art approaches.
HUAD: Hierarchical Urban Anomaly Detection Based on Spatio-Temporal Data

Due to the rapid development of communication and sensing technology, a large amount of mobile data is collected so that we can infer the complex movement laws of humans. For cities, some unusual events may endanger public safety. If the early warning of an abnormal event can be issued, it is of great application value to urban construction services. To detect urban anomalies, this paper proposes the Hierarchical Urban Anomaly Detection (HUAD) framework. The first step in this framework is to build rough anomaly characteristics that need to be calculated by some traffic flow consisted of subway and taxi data. In the second step, the alternative abnormal regions were obtained. Then, the long short-term memory (LSTM) network is used to predict the traffic to get the historical anomaly scores. Following that, the refined anomaly characteristics are generated from adjacent regions, adjacent periods and historical anomalies. The final abnormal regions were detected by One-Class Support Vector Machine (OC-SVM). At last, based on real data sets, we analyzes the traffic flow of the target region and adjacent regions from multiple perspectives in view of the large crowd gathering activities, and the effectiveness of the method is verified.
Traffic Sign Detection and Recognition Based on Convolutional Neural Network

Traffic sign recognition system (TSRS) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signs accurately and effectively can improve the driving safety. This paper brings forward a traffic sign recognition technique on the strength of deep learning, which mainly aims at the detection and classification of circular signs. Firstly, an image is preprocessed to highlight important information. Secondly, Hough Transform is used for detecting and locating areas. Finally, the detected road traffic signs are classified based on deep learning. In this article, a traffic sign detection and identification method on account of the image processing is proposed, which is combined with convolutional neural network (CNN) to sort traffic signs. On account of its high recognition rate, CNN can be used to realize various computer vision tasks. TensorFlow is used to implement CNN. In the German data sets, we are able to identify the circular symbol with more than 98.2% accuracy.
Entity Profiling in Knowledge Graphs

Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiless generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.
A Real Escape Gamification Teaching Activity Integrated with Situated Learning and Multi-dimensional Scaffolding for Elementary School Social Studies Course

The study aimed to design a real escape gamification teaching activity for elementary school social studies education. The teaching objectives is to guide students to learn and understand the culture and history of their hometown. The teaching activity uses slides and learning sheets to present situation with challenging tasks, and offers clues and opportunity for learners to participate problem-solving process in the real world. A total of 25 elementary school students were recruited to participate in the current research. The students were required to search information and answer the questions on the Internet via mobile devices. The results revealed that this learning activity enhanced students' learning performance, flow state, and game acceptance in social education.
Wind-Speed Estimation and Sensorless Control for SPMSG-Based WECS Using LMI-Based SMC

To enhance system reliability and to reduce the cost and complexity, this paper presents an efficient wind-speed estimation method and a sensorless rotor-position/speed control method for a surface-mounted permanent-magnet synchronous generator (SPMSG)-based variable-speed direct-drive wind-energy conversion system (WECS). In this regard, sliding mode control (SMC) based on a linear matrix inequality (LMI) is proposed to estimate the rotor position of an SPMSG. This method depends on measured electrical quantities, such as stator voltages and estimated stator current error, to evaluate the back electromotive force components, which are used to estimate the rotor position. The rotor speed is assessed according to the rate of change of the estimated rotor position. The wind speed is estimated by estimating the backpropagation power flow according to the nonlinear dynamical power wind-speed characteristics of the wind turbine. In estimating the rotor position, the proposed LMI-based SMC design provides good steady-state and dynamic performances under all operating modes of the WECS. The proposed control schemes are validated by simulating a test system, i.e., a 250-kW SPMSG-based WECS, in MATLAB/Simulink. The results confirm the effectiveness and correctness of the designed control schemes in estimating and tracking the actual rotor position/speed and wind speed with trivial errors.
Design and Implementation of small monitoring wireless network system based on LoRa

In this paper, we focus on the realization of a small environmental monitoring wireless ad hoc network sensor system based on LoRa(Long Range) which provides a real-time low-cost long-distance monitoring system. The system utilizes the Heltec WiFi LoRa 32 as both gateway and nodes. It adopts ESP32 chip as MCU which has a high level of low-power performance in the industry. The system integrates both LoRa module(SX1278) and WiFi module, which make the system combine the long-distance transmission and low power consumption of LoRa communication with the support of TCP/IP network communication. Our system can provide a user-friendly front end that is convenient for users real-time monitoring of the system. We use a unique set of self-organizing network protocols to meet the requirements of daily monitoring and emergency warning.
A Hybrid Intrusion Detection Algorithm Based on Gaussian Mixture Model and Nearest Neighbors

Hybrid learning approaches prove to be superior to single technologies in the field of intrusion detection. Moreover, the representative feature is a determinant of generating accurate signatures and improving detection performance. In this paper, we propose a hybrid intrusion detection algorithm based on Gaussian Mixture Model (GMM) and k-Nearest Neighbors (k-NN). More specifically, we first implement GMM to characterize the spatial distribution of each category. Then, a novel GMM-based data formation method is performed to extract distance and density features, called GMDD. By sufficiently utilizing the classification information contained in the original data, GMDD constructs concise and high-quality characteristics. Finally, k-NN is trained and tested on newly transformed datasets to build the detection model. The experimental results on KDD’99 and NSL-KDD datasets indicate that the proposed method not only outperforms other recent studies in terms of accuracy, detection rate and false alarm rate, but also provides excellent computational efficiency.
Realization of Electrically Small, Low-Profile Quasi-Isotropic Antenna Using 3D Printing Technology

A 3D printed, low-profile, electrically small antenna with a quasi-isotropic radiation pattern is presented herein. It is composed of an electric meandered dipole antenna, and the extended arcs from the meander line mimicking the current flow of the loop antenna. A quasi-isotropic radiation pattern is achieved from the total current flow over the proposed structure. Modern stereolithographic 3D printing and nano-polycrystalline copper coating technologies are used to build a prototype. The measured antenna exhibits a good uniformity in terms of the radiation pattern with a maximum gain deviation of 4.5 dB at 959 MHz and a radiation efficiency of 81 %, close to the computed expectations. The electrical size of the antenna <italic>ka</italic> is 0.48, and its height is <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula>/82.32.
A Survey of simultaneous localization and mapping for robot

In unknown environment, it’s very important to obtain pose of the robot and environment map through sensors such as camera and lidar carried by the robot itself. In this paper, several common slam algorithms are introduced, moreover, the advantages and disadvantages of different algorithms are also compared. Traditional slam algorithms mainly include filter-based slam and graph-based slam. The filter-based slam algorithm is mature and easy to understand. It is mainly based on extended Kalman filter (EKF-SLAM) and particle filter (PF-SLAM). However, the linearization error exists in EKF model and the complexity increases sharply when the moving range is large. Although PF-SLAM does not have linearization problem, it cost much to obtain high precision and the algorithm has problem of particle consumption. Slam based on graph optimization can obtain globally consistent pose and map, but the algorithm is ineffective when the environment is in the absence of texture information . The combination of traditional slam and deep learning can improve the robustness of slam algorithm, what’s more, the semantic map generated by deep learning has positive significance for intelligent navigation of robots. With the development of deep learning, sensors and computer technology, slam is bound to make further progress.
Optimal PID Tuning of BLDC Drive using LQR Technique

In this paper, we investigate systematically the method of getting optimal PID coefficient value for speed control of B rushless DC or ECM motor. The controller is used to control speed of BLDC or ECM drive in the forward motoring condition. The originality of this paper gives the idea about the attuning of PID controller with the help of conventional approach and Linear Quadratic Regulator (LQR) approach. The strategy of the linear quadratic regulator is employed to find the best and optimized coefficient parameters of the controller (PID and I-PD). The state vector augmentation for performance measurement involves output signals only. The Full State feedback method or pole placement method is used to determine the weight parameter function. The testing of the efficacy of LQR tuned controller (PID and I-PD) is done by comparing Simulink result with other different method for tuning PID. Both time domain analysis and frequency domain analysis is used for the comparative examination to find the effectiveness and the finest possible result obtained for the controlling speed of BLDC or ECM drive. The method of tuning the controller (PID and I-PD) gives the transient response is compared and analyzed for which the result is revealed in MATLAB Simulink. The resulting simulation of speed controlling system for BLDC or ECM motor can be used in Electric and Hybrid Electric automobile (EV and HEA).
Weak Supervised Surface Defect Detection Method Based on Selective Search and CAM

Due to the large scale variation of surface defects of different types of strip steel, there are limitations in using threshold segmentation to locate objects, we propose a surface defect detection algorithm combining selective search and class activation mapping (CAM) to improve objects localization. First, we use selective search to generate defect bounding box in the image, and predicts the classification and CAM of the defect in the image through the trained model. Then, in the defect detection, filter the bounding box with the classification information of the defect as priori knowledge. We only retain the bounding box that approximate the shape of the defect and map the filtered defect bounding box to the CAM of the corresponding defect. Finally, select the bounding box with the highest score as a detection result. Experiment results show that the proposed method can achieve a mean average precision of 91.1% on our dataset. And it can more accurately locate defects in the image. Compared with traditional CAM, our method has more excellent detection performance in surface defect detection applications of strip steel.
Automatic Cause Detection of Performance Problems in Web Applications

Abstract-The execution of similar units can be compared by their internal behaviors to determine the causes of their potential performance issues. For instance, by examining the internal behaviors of different fast or slow web requests more closely, and by clustering and comparing their internal executions, one can determine what causes some requests to run slowly or behave in unexpected ways. In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes. First, low-level and fine-grained information regarding each request is gathered by tracing both the user space and the kernel space. Second, further information is extracted and fed into an outlier detector. Finally, these outliers are then clustered by their behavior, and each group is analyzed separately. Experiments revealed that this pipeline is indeed able to detect slow web requests and provide additional insights into their true root causes. Notably, we were able to identify a real PHP cache contention issue using the proposed approach.
Deep Unsupervised Progressive Learning for Distant Domain Adaptation

The superiority of deeply learned representation has been reported in very recent literature of re-identification (Re-ID) task. In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL) for Re-ID task. Different from existing transfer learning problems which assume that there is a close relation between source domain and target domain, in the DDTL problem, target domain can be totally different from source domain. For example, the source domain classifies pedestrian images but the target domain distinguishes vehicle images. In this work, our goal is to execute an unseen and unrelated task based on a labeled dataset training previously without any samples from intermediate domains. Particularly, we consider the more pragmatic issue of learning a deep feature with no labels, and propose a Deep Unsupervised Progressive Learning (DUPL) method to transfer pretrained deep representations to unseen domains. Specifically, our work performs clustering and fine-tuning of the CNN to improve the performance of original model trained on the irrelevant labeled dataset. Empirical studies on distant domain adaptation task (pedestrian -> vehicle) demonstrate the effectiveness of the proposed method, and the improvement in terms of the mAP accuracy is up to 15% over "non-transfer" methods.
Multiple Target Localization Based on Binary Salp Swarm Algorithm optimized Compressive Sensing Reconstntction under WSNs

In this paper, a multiple target localization algorithm based on compressive sensing reconstruction of Binary Salp Swarm Algorithm (BSSA) is proposed to improve the multi-target positioning accuracy and anti-noise in wireless sensor networks. The continuous salp swarm algorithm is discretized in the binary space, and the essential characteristics of the rapid coordination change and foraging of the salp swarm are preserved, and then used for the reconstruction of compressive sensing signals to achieve multi-target positioning under the wireless sensor networks. The experimental results shows that compared with the traditional compressive sensing reconstruction algorithm, the algorithm has good noise immunity and counting performance. The positioning performance is better than the greedy matching pursuit(GMP) algorithm and the traditional l1-norm minimization algorithm.
Low-Complexity Analog Beamforming for mmWave Large-Scale MISOME Wiretap Channel

With the popularization of large-scale antenna arrays in wireless communication, the conventional fully digital beamforming, which requires dedicated radio frequency (RF) chain per antenna element, is not viable for large-scale antenna systems due to its prohibitively high hardware cost. On the contrary, the analog beamforming design only requires one RF chain for all antenna elements, and has been widely investigated in recent years. In this letter, we consider the secure analog beamforming design that maximizes the secrecy rate in the large-scale multi-input single-output multi-eavesdropper (MISOME) wiretap channel. However, the associated secrecy rate maximization (SRM) problem is naturally non-convex due to the constant envelope constraint induced by the analog beamformer. To handle the problem, a low-complexity algorithm that combines the Dinkelbach approach and iterative coordinate ascent (ICA) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results illustrate that the proposed algorithm achieves both higher secrecy rate performance and lower complexity as compared to the existing schemes.