Miracle-dz/UniFAR-FTUs huggingface dataset:
Application Research of Transmission Line Icing Monitoring Based on Optical Fiber Sensing Technology

Icing on the conductor is the main cause of accidents such as tower toppling and line breaking in winter, This has restricted the development of power grid to a great extent and brought huge economic losses to the country. With the construction of smart transmission lines, a variety of transmission line monitoring devices are widely deployed. The traditional icing monitoring device has some problems, such as complex installation, easy interruption of power supply and communication. This paper focuses on the application of optical fiber sensing technology in transmission line icing monitoring, On the one hand, the principle and technical characteristics of distributed optical fiber sensing based on Raman scattering, Rayleigh scattering and Brillouin scattering are analyzed, On the other hand, a transmission line icing monitoring network scheme based on multi parameter distributed optical fiber sensing is proposed, Finally, the prospect of distributed optical fiber sensing technology for transmission lines is put forward.
Cost Prediction of Distribution Network Project Based on DART Model

Strengthening lean management of distribution network construction is the most direct measure to ensure and improve people's livelihood and optimize user service requirements. It is also an inevitable requirement for building the international leading energy internet with Chinese characteristics. Due to the large number, relatively small scale, and diverse types of distribution network projects, it is difficult to predict the project cost. Based on the historical sample data of distribution network cost analysis, this paper constructs a distribution network project cost prediction model based on DART model, which can accurately predict the static investment of distribution network engineering. The research results can help investment decision makers quickly judge the rationality of the project cost at the initial stage of the feasibility study, improve the evaluation efficiency and cost accuracy, and effectively promote the standardization and lean management of the distribution network project cost.
Correlation analysis of database data based on the throughput coefficient

The data in the database with correlation is frequently separated into distinct data tables, making it difficult to query the target variable data of interest to users while also locating the correlated variable data. The throughput coefficient is used to propose a correlation analysis of database data in this research. The data of selected variables is first pre-processed, after which each variable is assessed using the pass-through coefficient and the corresponding variable association table is created in the database; finally, the method's practicality is tested by putting it into action on a web page. This research presents a novel approach to increasing the efficiency of database queries.
ASTS: Attention-based spectrum truncation synthesis for step frequency signals

With the development of wireless communication technology, various special pulse waveforms are widely used in radar technology to meet specific requirements. The stepped frequency (SF) signal has properties of low instantaneous band-width, high synthetic bandwidth, and high resolution, which is popular in high-precision target detection. Therefore, researches on SF signals have attracted extensive attention recently. In this paper, we introduced an attention mechanism into spectrum synthesis and finally proposed a novel method called Attention-based Spectrum Truncation Synthesis (ASTS) to solve the problem of energy fluctuation caused by spectrum aliasing and our ASTS method performs better than spectrum superposition synthesis (SSS) algorithm in Peak Sidelobe Ratio (PSLR). Furthermore, we found a noticeable phenomenon in detection caused by a dramatic phase jump happening with the RSF signals when detecting the moving targets. Through the theoretical analysis, we concluded formulas indicating how we could compensate for this phase jump and make the RSF achieve a similar PSLR performance as the LSF does.
Huber Loss Function Based on t-Distribution Yin-Yang-Pair Optimization Algorithm

Generally, all artificial intelligence (AI) algorithms can be eventually transformed into optimization problems for solving loss functions, and there are two common types of loss functions: mean squared error (MSE) and mean absolute error (MAE), which have their own advantages and disadvantages. The error between the data reflected by MAE is more accurate and stable, but its derivative is discontinuous and non differentiable, so its efficiency in solution process is low; MSE is differentiable, but it has the tend to enlarge and reduce the data error. The popular method in the field of AI is to use Huber loss function, which combines the advantages of both MAE and MSE, as a framework. Huber loss function is usually solved by gradient descent (GD) method or stochastic gradient descent (SGD) method, but these methods are easy to fall into local optimum and cannot find the global optimal solution. In this paper, a t-distribution Yin-Yang-Pair optimization algorithm has been proposed, such algorithm does not depend on gradient information and is easier to find the global optimal solution for Huber loss function, Via numerical experiment, the effectiveness of the proposed algorithm has been verified.
Research on Facial Feature Extraction Method Based on Semantic Segmentation

This paper obtains the contour map related to the facial features of the face by extracting important information such as facial features of the face, so as to complete the task of extracting the facial features of the face. With the rapid development of computer software and hardware technologies, facial feature extraction technology has become a research hotspot in computer image processing application scenarios such as face recognition and short video sketch filters. However, the current facial feature extraction technology based on deep learning still has problems such as insufficient contours of the extracted facial features and excessive noise. Therefore, aiming at the shortcomings of the original facial feature extraction methods, this paper proposes a facial feature extraction method based on semantic segmentation. The content of the Larticle is mainly expanded from the following main aspects: First, the development status of semantic segmentation is introduced. Then, an improvement to the semantic segmentation network model is proposed. Finally, the average intersection ratio is used as the evaluation index of the segmentation accuracy of the network model. By comparing the feature maps obtained by different methods, it is found that the improved semantic segmentation network model in this paper can realize the complete extraction of facial features.
3D Estimation of Single Image based on Homography Transformation

Aiming at the problem that it is difficult to extract three-dimensional information from a single plane image under the condition of lacking camera parameters, this paper proposes a scheme combining perspective transformation and pseudo inverse method to realize the rough extraction of three-dimensional information from a single image. On the basis of the existing geometric model of camera imaging, taking points from the two-dimensional image and calculating the perspective transformation matrix, the homography matrix is obtained, and then the basic parameters of the camera can be marked through the corresponding relationship between the matrix and the camera internal and external parameter matrix. Pseudo inverse method is the inverse process of camera imaging, that is, it can estimate the final three-dimensional space coordinates from the pixel coordinates of the captured plan through the pseudo inverse matrix, and realize the one-to-one correspondence between two-dimensional coordinates and three-dimensional coordinates. The experimental results show that the algorithm is fast and convenient. Compared with the traditional Zhang's calibration method, this calibration result error is less than 5%, and the execution time is shortened by 70%.
Some Innovative Drying Technologies for Dehydration of Foods

Thermal dehydration is the most common and cost-effective technique for preservation of foods and for the production of traditional as well as innovative processed products such as snacks with desired functionalities. This lecture will provide a global overview of emerging and innovative thermal drying technologies that are already commercialized or show potential of industrial exploitation upon successful R&D to sort out some limitations. Essentially new technologies are needed to enhance quality, reduce energy consumption, improve safety and reduce environmental impact. Mathematical modeling can be used for cost-effective development of untested novel designs to reduce the cost and time required for innovation. As examples of emerging drying technologies we will consider drying technologies with imminent commercialization potential. These include: heat-pump-assisted drying, microwave-assisted drying, low pressure superheated steam drying, pulse combustion spray drying, pulsed and ultrasound-assisted osmotic dehydration as well as novel gasparticle contactors such as impinging streams and pulsed fluidized beds. Multi-stage drying, intermittent drying and use of hybrid drying technologies that combine advantages of different dryers without some of their limitations will be outlined. This paper will also discuss different ways for energy minimization and potential for use of renewable energy e.g. solar, wind and biomass will also be discussed briefly.
Community Annotation Experiment Community Annotation Experiment for Ground Truth Generation for the i 2 b 2 Medication Challenge

Objective: Within the context of the Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records, the authors (also referred to as “the i2b2 medication challenge team” or “the i2b2 team” for short) organized a community annotation experiment. Design: For this experiment, the authors released annotation guidelines and a small set of annotated discharge summaries. They asked the participants of the Third i2b2 Workshop to annotate 10 discharge summaries per person; each discharge summary was annotated by two annotators from two different teams and a third annotator from a third team resolved disagreements. Measurements: In order to evaluate the reliability of the annotations thus produced, the authors measured community inter-annotator agreement and compared it to the inter-annotator agreement of expert annotators when both the community and the expert annotators generated ground truth based on pooled system outputs. For this purpose, the pool consisted of the three most densely populated automatic annotations of each record. The authors also compared the community inter-annotator agreement to expert inter-annotator agreement when the experts annotated raw records without using the pool. Finally, they measured the quality of the community ground truth by comparing it with the expert ground truth. Results and Conclusions: The authors found that the community annotators achieved comparable inter-annotator agreement to expert annotators, regardless of whether the experts annotated from the pool. Furthermore, the ground truth generated by the community obtained F-measures above 0.90 against the ground truth of the experts, indicating the value of the community as a source of high-quality ground truth even on intricate and domain-specific annotation tasks. Uzuner et al. Community Annotation Experiment
UAV visual flight control method based on deep reinforcement learning

Aiming at an intelligent perception and obstacle avoidance of UAV in an environment, a UAV visual flight control method based on deep reinforcement learning is proposed in this paper. The method employs Gate Recurrent Unit (GRU) to the UAV flight control decision network, and uses Deep Deterministic Policy Gradient (DDPG), a deep reinforcement learning algorithm to train the network. The special gates structure of GRU is utilized to memorize historical information, and acquire the variation law of the environment of UAV from the time series data including image information of obstacles, UAV position and speed information to realize a dynamic perception of obstacles. Moreover, the basic framework and training method of the network are introduced, and the generalization ability of the network is tested. The experimental results show that the proposed method has better generalization ability and better adaptability to the environment.
AR Tags Based Absolute Positioning System

The presented work describes the implementation of a system for positioning a mobile robot in a workspace using an AR (Augmented Reality) tag system. This was realized by means of a stereovision camera placed on top of the robot, which, with the aid of a gimbal (a two-joint camera reorientation system), had the ability to dynamically rotate in order to control the entire area around the robot. Markers with predetermined identification numbers were placed at specific locations in the robot’s work area. A topological map of the workspace was created. The robot determined its position based on this map. A simulation and an actual model of a 6-wheeled robotic platform were used to test the stereovision system. The experiment results came from the simulation performed in Gazebo software designed for such analyses and adapted to work with ROS (Robot Operating System) ecosystem. The results of the algorithm’s performance for both the simulation and the actual robotic platform were compared with each other. To verify the algorithm’s performance, tools available in both ROS and Gazebo (including rviz) were used.
Research and Design of Infrastructure Monitoring Platform of Intelligent High Speed Railway

With the improvement of technology and business management ability, especially the need of data governance, railway stakeholders have higher expectations and requirements for the level of railway infrastructure informatization. In order to support the development of railway infrastructure information, the monitoring platform will provide services for all the proposed projects, under construction projects, operation and maintenance projects of China Railway, which promotes the informationized and standardized management of infrastructure in the whole life cycle of railway construction, operation and maintenance. Monitoring platform adopts digital twin technology, integrates BIM+GIS model management, accesses to real-time Internet of things sensor data, establishes railway construction, operation and maintenance integrated monitoring big data center, realizes real-time monitoring of high-speed railway infrastructure. Through the virtual and reality mapping and interactive integration of all elements, all data, all models and all spaces, which realized the great transformation of high-speed railway management and operation from phenomenon driven to data driven, breaking through the key technologies of intelligent prediction and early warning of high-speed railway infrastructure, so as to better improve the service level and management efficiency of high-speed railway.
Construction of Knowledge Graph of Power Communication Planning based on Deep Learning

In order to improve the intelligent level of power communication network planning, a method for constructing a knowledge graph of power communication planning based on deep learning is proposed for the problems of lengthy power communication planning report text and low information extraction efficiency. This article takes the power communication planning text as the research object, constructs the knowledge organization structure of the power communication knowledge graph from top to bottom, and defines the entity concept and the relation concept. A variety of deep learning models are comprehensively used for knowledge extraction. Bi-LSTM-CRF model is used for named entity recognition, and PCNN model is used for entity relationship extraction, forming entity relationship table in power communication planning text. The effectiveness of the above method is verified by simulation experiment. Finally, the data storage and visualization are realized through Neo4j graph database.
Predication of remaining useful life of aircraft engines based on Multi-head Attention and LSTM

According to the large amount of high-dimensional time series data generated by sensors, and the insufficient utilization of time series information in the network model in the prediction on remaining useful life (RUL) of aircraft engines, a data-driven model for RUL prediction based on a Multi-head Attention mechanism and a long short-term memory neural network (LSTM) is proposed in this paper to optimize RUL of aircraft engine. The model can select the key features in the time-series data, then input them into the LSTM layer to mine the internal connections, and finally obtain RUL predicted results through two fully connected layers. Using the CMAPSS dataset provided by NASA for verification and comparing with other algorithms, the accuracy of this method outperforms shallow neural networks based on support vector regression (SVM) and deep learning methods such as convolutional neural networks (CNN), multi-layered LSTM and multi-layered BiLSTM, which provides a powerful support for health management of aircraft engines, operation and maintenance decisions.
Force-prediction Scheme for Precise Grip-lifting Movements

In this work we present a novel Supervised Learning scheme for executing sensitive Force-based manipulation tasks. The proposed scheme, termed SLF, is formulated as a three-stage process: (a) supervised trial-execution in simulation to acquire sufficient training data; (b) training to facilitate grasp learning with suitable robot-arm pose and lifting force; (c) grasp execution in simulation. Consequently, following sim-to-real transfer, operation in real environments is achieved in addition to simulated ones, generalizing also for objects not included in the trial sessions. The proposed learning scheme is demonstrated in object lifting tasks where the applied force varies for different objects with similar contact friction coefficients, and likewise the grasping pose. Experimental results on the manipulator YuMi show that the robot is able to effectively reproduce demanding lifting and manipulation tasks after learning is accomplished.
Tilburg University Charitable Giving , Emotions , and the Default Effect

We report an experiment to study the effect of defaults on charitable giving. In three different treatments, participants face varying default levels of donation. In three other treatments that are paired with the first three, they receive the same defaults, but are informed that defaults are thought to have an effect on their donation decisions. The emotional state of all individuals is monitored throughout the sessions using Facereading software, and some participants are required to report their emotional state after the donation decision. We find that the default level has no effect on donations, and informing individuals of the possible impact of defaults also has no effect. The decision to donate is independent of prior emotional state, unless specific subgroups of participants are considered. Donors experience a negative change in the valence of their emotional state subsequent to donating, when valence is measured with Facereading software. This contrasts with the selfreport data, in which donating correlates with a more positive reported subsequent emotional state. JEL Classification: C91
Arduino-based Line Tracking Smart Robot Car Equipped with YOLOv3 and Fuzzy Logic Controlled Ultraviolet Sterilizer

With the increasing number of cases of pathogenic diseases caused by airborne transmission of microorganisms, such as Staphylococcus aureus, interest when it comes to the disinfection of surfaces is increasing. One of the many devices that have been utilized for surface disinfection is the use of Ultraviolet Light radiation, specifically UVC radiation, as a sterilizer or disinfectant. In this study, a UVC disinfecting machine based on Arduino Nano was developed with a mobile application to control and enable the device's functionalities. The UVC disinfecting machine can be manually controlled via the generated mobile application and automatically navigated using the Line Tracking Algorithm. To ensure the safety of the people utilizing the device, the UVC disinfecting machine will be encircled with several motion sensors and a Wi-Fi camera module. The sensors and camera automatically disable its UVC lamp once it detects human presence by applying the algorithms of Fuzzy Logic and YOLOv3. This study provides a versatile, safe, and harmless UVC disinfecting machine while effectively disinfecting objects and surfaces.
Determinants of construction sector profitability in Croatia

This paper examines the determinants of profitability for construction companies in Croatia. Sample includes more than 8678 construction companies covering the period from 2003 to 2014 what present 11 years of observation including Croatian milestone in joining to the European Union as well as global financial crises. The authors analyze theoretical background and use empirical research to investigate the relation between profitability and selected determinants like price cost margin, concentration index, growth, size, material costs and lagged profitability. Special remarks are given with review of construction sector importance for the growth of the Croatian economy and repercussions of global financial crises. According to that, for the empirical part of the article generalized method of moments for dynamic panel model has been used which has given significant results. Overall, the results have proved a strong relationship between company size, concentration index, growth, lagged profitability and profitability besides number of employees.
eview : Catalytic oxidation of cellulose with nitroxyl radicals under queous conditions

2,2,6,6-Tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation is a unique reaction to native and regenerated celluloses, and has advantages in terms of position-selective reaction at room temperature under aqueous conditions. When the TEMPO/NaBr/NaClO oxidation is applied to native celluloses in water at pH 10 under suitable conditions, the C6-primary hydroxy groups present on crystalline cellulose microfibril surfaces are mostly converted to sodium C6-carboxylate groups. Anionic sodium glucuronosyl units are densely, regularly, and position-selectively formed on crystalline cellulose microfibril surfaces, while maintaining the original cellulose morphology, cellulose I crystal structure, crystallinity, and crystal width. When TEMPO-oxidized celluloses (TOCs) prepared from, for example, wood cellulose have sodium
Using Steerable Directional Antennas to Improve the Performances of IEEE 802 . 16 ( d ) Mesh Networks

The IEEE 802.16(d) mesh network is a promising next-generation wireless backbone network. This network requires that all network nodes should be equipped with an omni-directional antenna (or an directional antenna array emulating it). Exploiting the spatial reuse property of directional antennas in such networks is a great challenge. In this thesis, instead of collocating directional antennas to emulate omnidirectional antennas, we propose a novel design using only one steerable directional antenna for each node. Our design can better exploit the advantages of directional antennas and greatly reduce the deployment cost of the network. Our MAC-layer simulation results show that our design significantly increases the utilization of the control-plane bandwidth. The application throughput results show that our design can increase the aggregate TCP throughputs by a factor of 7.506 and the aggregate UDP throughputs by a factor of 2.436, respectively.