Miracle-dz/UniFAR-FTUs huggingface dataset:
PREDICTION OF PUBLIC MENTAL HEALTH USING MACHINE LEARNING ALGORITHMS

-Recently, a growing quantity of social media-associated intellectual issues (SNMD) have been recognized, consisting of cyber addiction, information overload and on-line compulsion. Today, the signs of those psychiatric issues are normally found passively, leading to a put off in medical intervention. In this newsletter, we argue that online social interaction analysis offers a possibility to proactively locate SNMD at an early stage. It is hard to pick out the SNMD because the mental elements taken into consideration inside the well-known diagnostic criteria (questionnaires) cannot be found inside the cuts of social pastime. Our approach, that is new and innovative in figuring out using SNMD, isn't always based on the self-identity of those intellectual elements via the use of questionnaires. Instead, we propose a machine learning framework, particularly Social Media Mental Disorder Detection (SNMDD), which makes use of features extracted from social media to accurately perceive ability cases of SNMD. We also use multi-supply learning in SNMD and advise a new tensor model (STM) primarily based on SNMD to improve overall performance. Our structure is evaluated via a consumer observe related to 3126 on line social community customers. We perform function analysis and follow SNMDD to huge datasets and function three sorts of SNMD evaluation. The consequences show that SNMDD is promising for figuring out social media customers with ability SNMDs.
A Programmable Dual Modulator Testbed for MIMO Applications

Multiple-input multiple-output (MIMO) systems use multiple transmitters and receivers to increase the capacity and reliability of radio frequency communication links used in multipath and disruptive environments. This paper describes a recently designed hardware testbed that can be used as a modulator and transmitter for MIMO systems which use two transmitters. The testbed consists of a field programmable gate array (FPGA) that generates the I/Q baseband signals for the two transmitters. A wide variety of modulation and coding formats, up to data rates of 10 Mbps, can be implemented by reprogramming the FPGA. The dual I/Q outputs from the FPGA are then fed to a pair of quadrature modulators, which have programmable carrier frequencies from 1,025 to 2,450 MHz. The system is implemented on a single printed circuit board, and has dual RF outputs with programmable power levels up to 0 dBm.
Design of a Gripper for Cable Assembly with Integrated In-hand Cable Manipulation Functions

Cable manipulation is widely used in industrial production (e.g., assembly of automotive wiring harnesses, wiring of electrical products etc.). Currently it is realized mainly by manual work in industry, which requires a lot of labor and time, and lacks flexibility. It is a challenge for a robot to operate cables, because when a robot grips a cable, it needs to perform many types of operations (e.g., twisting, dragging, sliding etc.) according to the pose and topological state of the cable. Traditionally these functions are realized by moving the robot arm with respect to the point where the cable is gripped. It is inefficient in many situations. To solve the aforementioned problems, in this paper, we propose a design of gripper integrated with functions for in-hand manipulation of cables. The validation experiments demonstrated its effectiveness and potential usage in solving many difficult problems involved in cable assembly.
Swinging Up and Balancing a Pendulum on a Vertically Moving Cart Using Reinforcement Learning

Underactuated systems occur frequently in robotics and legged locomotion. Unactuated pendulum on an actuated cart is a classic example used for designing and testing control algorithms for underactuated systems. While pendulum balancing on a horizontally moving cart is popular and environments available for reinforcement learning, pendulum on vertically moving cart is rarely discussed due to relatively higher difficulty level in balancing it. This paper presents a model environment for a pendulum on a vertically moving cart and trains a neural network controller using reinforcement learning to balance it in vertical position indefinitely without exceeding the displacement limits. Results presented for both con-tinuous and discrete force control input for the cart system show that the neural network controllers can successfully swing up and balance the pendulum.
Neural Network Approach to Dissolved Gas Analysis for Fault Analysis in Power Transformers

In this paper, an application on artificial neural network (ANN) named Levenberg Marquardt backpropagation training algorithm as multi-layer reverse diffusion ANN approaching to Dissolved Gas Analysis (DGA) was implemented to utilize a computational and automatic internal fault prediction of power transformer oil insulation. The DGA method applying Key Gas method and Duval Triangle method is used to identify such type of faults. The RadBas, Tan-Sigmoid, Log-sigmoid, Purelin linear transfer functions were chosen to find mean absolute error and least learning time during the analysis. The DGA results of 244 power transformers in distribution system were used as training data while additional DGA results of 100 power transformers were tested and compared to manual analysis. The results show that there is 6.4% absolute error from manual fault prediction. The method and tool are appropriate and less time consuming for condition-based maintenance of power transformer.
Visual Place Recognition via Semantic and Geometric Descriptor for Automated Valet Parking

Visual place recognition refers to identifying whether the current visual observation is from a place in a pre-built map and if so, which one. Accurate place recognition improves the localization accuracy of autonomous vehicles and helps them to park at designated places. Existing methods perform unsatisfactorily in AVP tasks due to the sparse texture of the parking lot, the varying viewpoints and the moving cars. To alleviate these problem, this paper proposes a robust and discriminative descriptor by encoding semantic information and geometric arrangement. A coarse-to-fine place recognition framework is also presented. Coarse place recognition candidates are identified by semantic information. Then, spatial consistency is performed to enhance the previous coarse results. Experiments in a parking lot demonstrate that the proposed framework achieves better performance where existing state-of-the-art methods fail.
A Geometric Assistive Controller for the Users of Wheeled Mobile Robots without Desired States

Wheeled mobile robots e.g., wheelchairs, can depend on indirect and discrete joystick controls from users. Correct steering angle becomes more important when the user should determine the vehicle direction in high velocities. Also, the velocity of the semi-automated vehicle can be given with constant patterns. These issues urge a new assistive controller with different objectives to address these problems. Moreover, the conventional control methods mostly require a desired states or reference trajectory beforehand which completely contradicts how humans operate with spontaneous decisions. In this paper, we propose a novel assistive control strategy to improve vehicle locomotion without using reference states. First, we explain the vehicle kinematics and considered Darboux frame kinematics on a contact point of a virtual wheel and plane. Next, the geometric controller using the Darboux frame kinematics is developed for having smooth trajectories under certain safety objectives. We check our findings in the simulation space with different case studies. Another advantage of this controller is using geometric formulations rather than complex algorithms that make it computationally efficient.
A Novel Unmanned Surface Vehicle with 2D-3D Fused Perception and Obstacle Avoidance Module

Unmanned surface vehicles (USVs) are important intelligent equipment that can accomplish various tasks on open area marine. During operation, environmental perception and obstacle avoidance is of vital significance to its autonomy. In this paper, we propose a novel USV equipped with fused perception and obstacle avoidance module that contains robust perception, localization and effective obstacle avoidance strategy. The new module is named Three-Dimensional Perception Module (PMTD), which utilizes camera and LiDAR to integrate multi-dimensional environmental information. It is able to detect, identify and track target objects in the process of autonomous travel. The localization precision achieves a centimeter-level with GPS and IMU devices. Meanwhile, the obstacle avoidance strategy allows the USV to efficiently keep away from static and dynamic floating objects in water areas. Through real-world experiments, we show that with the help of the proposed module, the USV can complete stable and autonomous operation and obstacles avoidance path planning even without any manual intervention. This indicates the strong ability of the module in autonomous driving for USVs.
Using Scanner Data for Computing Consumer Spatial Price Indexes at Regional Level: An Empirical Application for Grocery Products in Italy

Abstract The importance of constructing sub-national spatial price indexes (SPIs) has been acknowledged in the literature for over two decades. However, systematic attempts to compile sub-national SPIs on a regular basis have been hampered by the labour-intensive analyses required for processing traditional price data. In the case of household consumption expenditures, the increasing availability of big data may change the current approach for estimating sub-national SPIs by considering the use of weighted index formulae. The aim of this paper is twofold: firstly, to review previous literature on sub-national SPIs and secondly to estimate Italian consumer SPIs. To this aim we use scanner data referring to grocery products sold in a random sample of approximately 1,800 Italian outlets belonging to the most important retail chains and including information on prices, quantities and quality characteristics of products at barcode level. Various weighted index formulas are used for calculating consumer SPIs at detailed territorial level and at the lowest aggregation level. Our results show an interesting territorial variability of consumer prices of products sold in large-scale retail outlets across the Italian regions. Overall, the Southern regions appear to have price levels below the national average both for food and non-food products with some interesting exceptions.
Archaeology and Virtual Simulation Restoration of Costumes in the Han Xizai Banquet Painting

Abstract Based on 3D virtual fitting technology, this paper simulates and reproduces the fabric patterns and sewing processes of 12 characters’ costumes in different scenes on the basis of completing the archaeology of the characters’ costumes in the painting, so as to realize the 3D virtual sewing and digital simulation restoration of the characters costumes. This paper draws the style diagram, structure diagram and 3D virtual simulation diagram of the character costumes in the painting. The article further improves the research on the costumes of the Five Dynasties and Ten Kingdoms, which has a certain reference value for the study of ancient character costumes and the promotion of Chinese garment culture. At the same time, it provides a reference for the design of artistic works such as character costumes in film and television and games.
Statistics in diagnostic medicine

Abstract This tutorial gives an introduction into statistical methods for diagnostic medicine. The validity of a diagnostic test can be assessed using sensitivity and specificity which are defined for a binary diagnostic test with known reference or gold standard. As an example we use Procalcitonin with a cut off value ≥ 0.5 g/L as a test and Sepsis-2 criteria as a reference standard for the diagnosis of sepsis. Next likelihood ratios are introduced which combine the information given by sensitivity and specificity. For these measures the construction of confidence intervals is demonstrated. Then, we introduce predictive values using Bayes’ theorem. Predictive values are sometimes difficult to communicate. This can be improved using natural frequencies which are applied to our example. Procalcitonin is actually a continuous biomarker, hence we introduce the use of receiver operator curves (ROC) and the area under the curve (AUC). Finally we discuss sample size estimation for diagnostic studies. In order to show how to apply these concepts in practice we explain how to use the freely available software R.
Model-Predictive Optimization for Lane Keeping Assistance System with Exponential Decay Smoothing

Lane keeping assistance system is a widely used driving assistance system. When the vehicle enters and exits a curve, the responses related to lateral stability will show significant fluctuations. Smooth lane keeping is the cornerstone of the user’s trust towards the lane keeping assistance, and it is important to smooth the responses related to lateral stability. Therefore, in this paper, a lane keeping control strategy that can achieve smooth steering operation is proposed. First of all, the lateral dynamics model that can be used for lane keeping control conveniently is established. In addition, with the model predictive control (MPC), the control strategy for lane keeping is designed. The main control objective is lateral stability, and performance variables are used to measure the performance during lane keeping process. What’s more, the contribution of this paper is that a reference trajectory in the form of an exponential decay function is set for the performance variables, so that the performance variables approach the optimal value in a smooth manner. Finally, two simulation scenarios for lane keeping are set up, and the control strategy in this paper is verified by simulation experiments.
Design of an integrated streak camera based on a time correlated single photon counting system

In this work we present a TCSPC Streak Camera (TCSPC-SC) that takes advantage of the streak mode imaging ta overcome the space limitation inherent ta 20 TCSPC sensor arrays. This cost-effective solution allows the integration of complex functionalities in the pixel without the inconvenience of low fill factor that leads ta low detection efficiency. The TCSPC~SC consists of two main building blacks: a SPAD and a time measurement black bath integrated in 180 nm Standard CMOS technology. The SPAD was selected among 6 different SPAD structures following a thorough characterization process ta fully determine its performance figures. The time measurement black consists of a hybrid TOC capable of achieving high adjustable time resolutions with large dynamic range owing ta a time conversion scheme that combines traditional Analog Time to Amplitude Converter (TAC), Digital DLL-based and counter-based TOC. Furthermore, thehybrid TOC was especially designed ta be used in a TCSPC system that incorporates an array of TDCs which required a careful design ta limit power consumption and occupation area in order to achieve a flexible and easily scalable architecture. These two building blacks were bath fabricated in a 180 nm standard CMOS technology and employed ta demonstrate a TCSPC Streak Camera(TCSPC-SC) test structure that englobes 8 units in order ta demonstrate the system's operation principle with the final aim of implementing a complete and bigger TCSPC-SC model in the near future
Design and Development of a Thin Conformal C-Band Telemetry Antenna for a Small Diameter Missile

This paper will present the preliminary design of a C-Band telemetry antenna mounted conformal to a small diameter missile. Various design studies and options will be explored leading to a preliminary design that best meets system requirements. Simulation results are presented for various options and the rationale for down selection to final configuration is discussed. 1.0 INTRODUCTION This paper will describe the preliminary design and development of a thin conformal mounted CBand telemetry antenna designed for use on a small diameter missile. Since C-Band has become available for airborne telemetry applications, there has been interest in developing replacement telemetry antennas for missile applications. This paper will describe the design process used to develop a new telemetry antenna for use on a small diameter supersonic air launched missile. The operating band for this design is 4400 to 5150 MHz, which covers both the lower ( 4400 to 4940 MHz) and mid C-Band telemetry frequencies (5091 to 5150 MHz). The design of a telemetry antenna for C-Band is of interest to the test and evaluation community because C-Band is a new band available whereas L and S Band were commonly used for this purpose. The design challenges of implementing the antenna at these higher frequencies will be described in this paper. This paper will first describe the requirements for the C-Band telemetry antenna, which are the same for the legacy antenna, except the frequency band is now 4400 to 5150 MHz instead of 2200 to 2290 MHz. The traditional approach has been to utilize some type of microwave patch or slot as the radiator either alone or as part of an array with a feed network to a common port. The paper will then describe various radiating elements that were considered and present some simulation results. Also, various options for the feed network will be described. Finally, a candidate design will be selected from among the various options and future prototyping efforts will be described. Distribution: Statement A" Approved for public release; distribution unlimited" 1
Development of an Embedding Framework for Clustering Scientific Papers

In this era, research and development is becoming a continuous and accelerating process due to the fact that the technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends;. In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field, or language, but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.
Grasping Force Control for a Soft Finger with SMA Actuator based on Inverse Models and Lag Compensations

The study focus on the grasping force control for a soft finger actuated by shape memory alloy (SMA) based on model prediction and lag compensations. A test scheme of the grasping force control is presented and a series of experiments, which consist of step command experiments and sinusoidal command experiments, are investigated. The experimental results indicate that the control performance of predictive controller based on mathematic model is better than that of PIP-P3 and PID controller.
An Underwater Soft Claw Based on Bionic Principle

The marine products are rich, nurturing many creatures on the earth and providing precious research objects for human scientific exploration. The surface of some marine animals is fragile and easily damaged. Animals such as sea cucumbers may even deform when they are grasped by external forces, making it extremely difficult to grasp. In this regard, this paper proposes a bionic soft gripper and details the design and modeling methods. The bionic claw adopts a three-finger structure. The finger design adopts a bionic shape with a deformation layer and a limiting layer. It is molded with soft silicone material. The maximum bending displacement of a single soft finger can reach 55 mm. The soft hand is lighter in weight and smaller in driving force. The control system is more portable than rigid claws. A simple push rod can be used to increase/decrease pressure to achieve positive and negative pressure driving. The gripping action of the gripper can be realized by driving water or air, and by adjusting the fixed position of the claw on the flange, the target objects of different sizes can be dealt with. The applicable objects are diverse and the application scenarios are wide.
A Review On Social Media Crime Related Users Prediction Methodology

Data mining may be used for modelling, identifying, and preventing criminal behavior. Criminology is the study of criminal behaviour’s characteristics. Crime analysis, in fact, entails investigating and identifying crimes, as well as their relationships to offenders. Because of the large quantity of crime records and the complexity of the links between various forms of data, criminology is a good field for using data mining methods. Several Machine Learning methodologies, such as supervised, semi-supervised, and unsupervised learning, are used on crime data to help improving projected crime accuracy. This research will be beneficial to local police stations in terms of crime prevention. The outcomes of system give idea about crime prediction and prevention. Several feature extractions approaches, as well as Machine Learning methodology and their advantages, will be discussed in this paper. There are also some drawbacks mentioned in previous research study. This paper also gives suggestions for future research on Predicting Criminal Behavior on social media.
A Novel Hybrid Attitude Fusion Method Based on LSTM Neural Network for Unmanned Aerial Vehicle

Unmanned aerial vehicle (UAV) is a highly coupled and multivariable nonlinear complex system, the attitude fusion of which is often disturbed by the noise of low-cost inertial measurement unit (IMU). In order to improve the UAV attitude fusion precision, a novel hybrid attitude fusion method is introduced in this paper. An error model between the IMU output and attitude is established by a long short term memory neural network (LSTM NN). The attitude fusion error can be estimated well by using the powerful nonlinear fitting and time series processing ability of the LSTM. The experimental results demonstrate that the root mean square error (RMSE) of pitch, roll and yaw can be reduced to 0.69 degrees, 0.73 degrees and 0.59 degrees respectively, which effectively improve the accuracy of attitude fusion.
Design and Experiment of a Soft Gripper Based on Cable-Driven Continuum Structures

Soft grippers can be used as the end effectors of robots due to their good adaptability to the object being grasped. This is because of the passive compliance of the material. However, for this reason, the grippers have difficulties applying enough forces to the objects. Therefore, how to design the grippers to realize universal adaptive and compliant grasping of the object is worthy of study. In this paper, we design a novel cable-driven soft gripper, the shape and function of which are similar to a human hand. It is able to realize a stable self-adaptive grasp. Kinematic and kinetic models of the finger are established, simulation analysis and finite element analysis are carried out to optimize the finger. Experimental validation proves that the gripper designed can realize stable self-adaptive grasping.