Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Recognition System of Substation Hard Platen State Based on Machine Learning

According to the existing problems of substation hard platen state recognition, this paper studies the intelligent recognition system based on machine learning without modifying the secondary device. A manual patrol inspection car is designed to facilitate image acquisition. Using the hard platen state recognition algorithm based on machine learning, a hard platen intelligent patrolling application is developed. The current state of the hard platens can be quickly obtained by simply input the collected hard platen image. In addition, the patrol database is set up, and the hard platen table is kept, and the patrol and maintenance tasks can be managed. The experiment of this system was carried out at a substation in Guizhou. The results show that the system can recognize the state of the hard platens quickly and accurately, and truly realize the intelligence of the hard platen inspection work.
A Platform for the Evaluation of Distributed Reputation Algorithms

In distributed environments, where unknown entities cooperate to achieve complex goals, intelligent techniques for estimating agents' truthfulness are required. Distributed Reputation Management Systems (RMSs) allow to accomplish this task without the need for a central entity that may represent a bottleneck and a single point of failure. The design of a distributed RMS is a challenging task due to a multitude of factors that could impact on its performances. In order to support the researcher in evaluating the RMS robustness against security attacks since its beginning design phase, in this work we present a distributed simulation environment that allows to model both the agent's behaviors and the logic of the RMS itself. Moreover, in order to compare at simulation time the performance of the designed distributed RMS with a baseline obtained by an ideal RMS, we introduce an omniscient process called truth-holder which owns a global knowledge all involved entities. The effectiveness of our platform was proved by a set of experiments aimed at measuring the vulnerability of a RMS to a common set of security attacks.
Root Cause Identification Approach Based on FP-Growth for Product Quality Accident

The Product Quality Accident (PQA) is a kind of accident mainly caused by product quality defects originated in design and production. To decrease the number and the severity of PQA in usage is a routine task of quality and reliability engineer. Especially with the advent of the era of intelligent manufacturing and big data, the functional structure of product is becoming increasingly complicated and the dimension of big data in product lifecycle is high. As a result, it is difficult to artificially identify the root cause of PQA by traditional method. Therefore, this paper proposes a novel method based on an advanced data mining algorithm for root cause identification. Firstly, the quality accident formation mechanism is introduced in detail. Secondly, with the aid of the domain mapping theory, PQA relevance tree is contributed. Thirdly, with the setting of reasonable support and confidence, the FP-Growth algorithm is used to further mine the relevance rules and construct a complete PQA relevance tree, which could reduce the ambiguity of PQA root cause identification. Finally, the effectiveness of the proposed technique is verified by a root cause analysis example of an engine quality accident.
Octree map based on sparse point cloud and heuristic probability distribution for labeled images

To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high level contextual understanding of the surroundings is necessary to execute accurate driving maneuvers. This paper presents a novel approach to build three dimensional semantic octree maps from lidar scans and the output of a convolutional neural network (CNN) to obtain the labels of the environment. We present a heuristic method to associate uncertainties to the labels from the images based on a combination of the labels themselves, score maps retrieved by the CNN and the raw images. These uncertainties and the camera-lidar calibration parameters for multiple cameras are considered in the projection of the labels and their uncertainties into the point cloud. Every labeled lidar scan works as an input to an octree map building algorithm that calculates and updates the label probabilities of the voxels in the map. This paper also presents a qualitative and quantitative evaluation of accuracy, analyzing projection in single lidar scans and complete maps built with our probabilistic octree framework.
Environmental Monitoring Using Low-Cost Hardware and Infrastructureless Wireless Communication

Infrastructureless wireless sensing is beneficial for environmental monitoring in various application scenarios, such as in a disaster where it is vital for the operational planning of professional responders, or in documenting long-term changes in climate and biodiversity in natural heritage sites. In this paper, we present a low-cost yet flexible and powerful hardware/software platform for sensing, computation, and infrastructureless wireless communication. Since energy usage is a key issue for autonomous operation, we investigate the power requirements of various computing platforms, sensors and radio link technologies. Furthermore, we experimentally evaluate several license-free radios (WiFi, Bluetooth, LoRa 433 MHz and 868 Mhz) in terms of their real-world communication ranges. To avoid wasting precious communication resources, we apply machine learning and image-based concept detection on-device to remove irrelevant data before sending it. We also evaluate neural compute sticks to improve performance and optimize their power consumption in our scenario. The proposed hardware/software platform consists of small, low-power sensor nodes based on microcontrollers, larger single board computer relay nodes that can also preprocess data, and Bluetooth Low Energy enabled smartphone add-ons to give mobile devices access to long range communication technology. Finally, a Linux distribution tailored to the specific needs in this application area is presented that makes deploying new relay nodes easy even for non-specialists.
The Effects of a Software Artefact Designed to Stimulate Abstract Thinking Ability on the Academic Performance in Object Oriented Programming of First Year Information Technology Students

A below average throughput of Information Technology students specializing in software development is a challenge that many Universities and Universities of Technology in South Africa face. Contributing factors to this phenomenon are varied at best, but one of the identified factors are that students in this field, especially first year students, find it difficult to conceptualize the associated information and manner of thinking required to become successful in their studies. This is especially true when considering object orientated programming concepts and paradigms that students are required to master as part of their studies. Literary evidence suggests that a high level of working memory, which is associated with abstract thinking ability, is required when learning and applying object orientated programming concepts. The problem becomes more evident and serious if we consider that the Information and Communication Technology sector of a country is largely dependent on the graduating student populous in terms of growing the sector sustainably. A specialized software instrument was developed and tested in an attempt to affect a change in the abstract thinking ability of students from a student sample at a University of Technology. The results of this study focusses on the effect that the instrument realized on the academic performance of first year students related to particularly to object orientated programming and their abstract thinking ability in general as gauged by, amongst other instruments, the General Scholastic Ability Test, or GSAT, rather than focusing on the instrument itself.
Energy-Efficient Design and Control of a Vibro-Driven Robot

Vibro-driven robotic (VDR) systems use stick-slip motions for locomotion. Due to the underactuated nature of the system, efficient design and control are still open problems. We present a new energy preserving design based on a spring-augmented pendulum. We indirectly control the friction-induced stick-slip motions by exploiting the passive dynamics in order to achieve an improvement in overall travelling distance and energy efficiency. Both collocated and non-collocated constraint conditions are elaborately analysed and considered to obtain a desired trajectory generation profile. For tracking control, we develop a partial feedback controller for the driving pendulum which counteracts the dynamic contributions from the platform. Comparative simulation studies show the effectiveness and intriguing performance of the proposed approach, while its feasibility is experimentally verified through a physical robot. Our robot is to the best of our knowledge the first nonlinear-motion prototype in literature towards the VDR systems.
Action Selection for Interactive Object Segmentation in Clutter

Robots operating in human environments are often required to recognise, grasp and manipulate objects. Identifying the locations of objects amongst their complex surroundings is therefore an important capability. However, when environments are unstructured and cluttered, as is typical for indoor human environments, reliable and accurate object segmentation is not always possible because the scene representation is often incomplete or ambiguous. We overcome the limitations of static object segmentation by enabling a robot to directly interact with the scene with non-prehensile actions. Our method does not rely on object models to infer object existence. Rather, interaction induces scene motion and this provides an additional clue for associating observed parts to the same object. We use a probabilistic segmentation framework in order to identify segmentation uncertainty. This uncertainty is then used to guide a robot while it manipulates the scene. Our probabilistic segmentation approach recursively updates the segmentation given the motion cues and the segmentation is monitored during interaction, thus providing online feedback. Experiments performed with RGB-D data show that the additional source of information from motion enables more certain object segmentation that was otherwise ambiguous. We then show that our interaction approach based on segmentation uncertainty maintains higher quality segmentation than competing methods with increasing clutter.
Structure preserving Multi-Contact Balance Control for Series-Elastic and Visco-Elastic Humanoid Robots

This paper proposes an integration of multi-body and actuator control for multi-contact balancing for robots with highly elastic joints. Inspired by the structure preserving control concept for series-elastic fixed-base robots, the presented approach aims to minimize the control effort by keeping the system structure intact. Balancing on multiple contacts requires to solve the force distribution problem. In locomotion, contacts change quickly, requiring a swift redistribution of contact forces. This is a challenge for elastic robots as the actuator dynamics and limits prevent instantaneous changes of contact forces. The proposed dynamically consistent force distribution is implemented as a model predictive controller which resolves redundancy while complying with contact force and actuator constraints.
Rationale for Principles of Developing Control and Protection of Web Content Using CMS Drupal

The paper describes the solution of the problem of the porting content from the old website to the new platform with added interactive functionality. Made the rationale for the selection of the content management system Drupal-based on the analysis of the comparative characteristics of the other CMS. The paper provides detailed practical implementation of the desired functionality. A brief overview of the classes of functional requirements for information security is given. On the example of one of the classes the adaptation of the ranking method to assess the probability of unauthorized access taking into account the factors formulated. For each factor are valid values. The analysis of the password length depending on the size of the original alphabet of characters. Probabilistic estimates of hacking are calculated, recommendations on the optimal combination are given.
Size Reduction of Self-Isolated MIMO Antenna System for 5G Mobile Phone Applications

An antenna element that has not only the self-isolated property but also a compact size is proposed for the fifth-generation multiple-input and multiple-output (MIMO) system in mobile phone applications. In particular, the size reduction is done by introducing two vertical stubs into the original self-isolated antenna element. It is demonstrated that very good isolation and antenna efficiency for an 8-antenna MIMO system can be obtained without using any additional isolation elements or decoupling techniques. An antenna prototype is fabricated and measured; and a quite good agreement between simulation and measurement is obtained.
Advanced Multiplier Design and Implementation using Hancarlson Adder

Finite Impulse Response (FIR) filters, microprocessors and digital signal processors are the core system of multipliers. The Multiplier and Accumulator Unit (MAC) is the predominant block in a Digital Signal Processing (DSP) system. The objective of MAC is implementation of signal processing with high performance but multiplier most of the time occupies much area and become power consuming circuits. In this paper, a Modified Russian Peasant Multiplier (MRPM) using Hancarlson adder (HA) has been proposed. According to Russian Rules, a Divide and conquer technique is used in the multiplication process. But, in the perspective of digital design, only shifters and adders are used in the Russian Peasant Multiplier (RPM) to produce a Partial Product Generation (PPG). In this paper, we present an approach towards the reduction of delay in existing RPM by using HA, in the partial product reduction stage and proposed RPM with HA at Partial Product Addition (PPA). The proposed design is also compared to the RPM with Ripple Carry Adder (RCA), Carry Selector Adder (CSA) and 8-2 Adder Compressors (AC) in terms of propagation delay. The proposed design enhances speed of the system by 80.4% compared to the RPM using RCA, 81.7% compared to RPM using CSA and 77.5% compare to RPM with 8:2 adder compressors (AC’s). The total operation is coded with Verilog Hardware Description language (HDL) using Model-Sim 6.3C, synthesized by using the Xilinx Integrated Software Environment (ISE) 14.7 design tool.
Friendly Motion Learning towards Sustainable Human Robot Interaction

For generating interactive behavior of robot to build a long-term relationship between humans and robots, we focus on the difference in familiarity of the human behaviors during conversation. It is difficult to extract interaction motion features correlated to such familiarity as a model in manual. Therefore, we use a machine learning technique: convolution neural network to learn and generate interaction behavior with different familiarity. In the evaluation experiment, we generated interaction behavior using a convolution neural network, which learned from the behaviors of friendship and unknown relationship, who have high and low familiarity respectively. We evaluated how much such interaction behavior affect the human impression by questionnaire survey.
Algorithmic and Software to Identify Errors in Measuring Equipment During the Formation of Permanent Joints

The errors in the means of measuring technology have a significant impact on the technological processes of creating permanent joints. This is also true for the processes of creating permanent joints based on induction heating. In this technological process take place an induction heating of materials with different values of emissivity, which introduces significant errors in the indications of measuring equipment. Non-contact pyrometric sensors are used to take readings of the induction soldering process, which also causes the occurrence of a large number of factors that introduce errors into the readings of these sensors. To ensure high quality control of the induction soldering process, it is necessary to carry out identification of errors. This article suggests the use of intelligent methods for identifying errors in measuring instruments. As an algorithmic support, it is proposed to use the apparatus of artificial neural networks. The software offers the use of the programming language “Python” and the set of libraries “Anaconda”. In the article is described the process of effective neural network structure and parameters search. The results of experimental study confirm that the usage of intelligent methods for identifying errors allows to improve the quality of induction soldering process control as well as other methods of permanent joints creation.
Agent-based Power System Time-domain Simulation Considering Uncertainty

This paper proposes a scalable and flexible framework for power system time-domain simulation considering uncertainty. In the proposed framework, the power system uncertainties, including load variation, power generation of renewable energy sources and contingencies caused by faults are modeled using agent-based simulation techniques. In addition, communication between agents are carefully dealt with. The IEEE 14-bus test system is employed to test the proposed method and the results are compared with those obtained from the conventional Monte-Carlo based method. It is illustrated that the proposed method is accurate and feasible in uncertainty analysis, and is more suitable for modern power system transient stability analysis.
Complete and Efficient DRAT Proof Checking

DRAT proofs have become the standard for verifying unsatisfiability proofs emitted by modern SAT solvers. However, recent work showed that the specification of the format differs from its implementation in existing tools due to optimizations necessary for efficiency. Although such differences do not compromise soundness of DRAT checkers, the sets of correct proofs according to the specification and to the implementation are incomparable. We discuss how it is possible to design DRAT checkers faithful to the specification by carefully modifying the standard optimization techniques. We implemented such modifications in a configurable DRAT checker. Our experimental results show negligible overhead due to these modifications, suggesting that efficient verification of the DRAT specification is possible. Furthermore, we show that the differences between specification and implementation of DRAT often arise in practice.
A Trend Monitoring Diagnostic Algorithm for Automatic Pre-identification of Turbofan Engines Anomaly

Each day a medium range aircraft schedules almost ten flights. During those flights each aircraft may send data to the ground via a satellite communication channel. With more than 35000 CFM engines flying every day Safran Aircraft Engines receives a big amount of such messages. However, all messages do not imply an immediate maintenance action. Most of them are already managed by preventive maintenance operations. On 1000 messages managed each day by fleet operators, only 10 of them lead to airline reports. To select the 1% important messages, a fleet manager plots some data curves collected during the last flights, if the operator sees a specific pattern he may decide to transfer the alert and write a report. As we have more and more engines in operation it becomes necessary to helps the operators with some diagnostic tool. Hence we build a detection algorithm that automatically analyses the multidimensional data curves and uses a supervised learning process to select a good combinations of tests giving more than 85% of good pre-identification. As the expert operator may also have some degree of liberty in his decision, such performance is considered as good enough for a pilot implementation.
Development of Camber-Flat Wing Structure Convert Mechanism for Asymmetric Flapping Micro Air Vehicle

This study presents principle of the camber-flat wing structure conversion mechanism, which is inspired by a dragonfly, and its applicability to MAV. The camber-flat wing structure convert mechanism makes MAV flight using asymmetric flapping pattern through control of angle of attack without complicate structure. This mechanism was inspired from the dragonfly's feature that the camber structure of the wing increases the rigidity of wing structure and makes dragonfly has asymmetric flapping pattern. Experimental results show that MAV has asymmetric flapping pattern that can more stable flight performance when hovering flight with a camber structure and superior performance when the forward flight with a flat structure. The average lift force in the camber wing structure was 0.02N, the average thrust force was 0.02N and the average lift force was 0.011N in the flat wing structure at 20 Hz flapping frequency.
Muscle Activation Source Model-based sEMG Signal Decomposition and Recognition of Interface Rotation

Muscle activation signals are measured from the skin surface as surface electromyography (EMG) signals that contain information on human intentions; therefore, they are widely used in various robotics applications owing to their usability. However, selective muscle activation extraction is difficult because of the complexity of muscle structures. This study investigated muscle activation source model-based sEMG signal decomposition that considers the anatomical factors of muscle structures. The main advantage of the proposed model-based signal decomposition is that sEMG interface rotation can be recognized by comparing source parameters identified before and after rotation. To assess the performance of the proposed model-based decomposition method, hand motion estimation and rotation recognition were conducted. Additionally, two-dimensional simultaneous control was conducted with an inertial measurement unit to verify the usability of the proposed model. The results indicate that the proposed model decomposes an sEMG signal based on motion with good performance and demonstrate feasibility of motion estimation independent of sEMG interface rotation.
Development and Experimental Testing of Microstrip Patch Antenna-Inspired RF Probes for 14 T MRI Scanners

Radio frequency (RF) probes for ultrahigh-field magnetic resonance imaging (MRI) systems were developed from structures inspired by microstrip patch antennas. The patch structures provide an alternative to conventional surface coils. We show that these structures, operating as resonators, can produce strong magnetic fields, which act as B1 fields in MRI studies. The addition of high permittivity inserts to the patch substrate was beneficial for increasing B1 field uniformity. It was also shown by simulations that two vis-à-vis placed identical patches fed with 180° phase difference could produce uniform B1 field in the space between patches and could be used as volume RF probes. This possibility was confirmed by experiments with fabricated prototypes in the VARIAN 14.1 T MRI scanner. We further show that the B1 uniformity is enhanced over larger volume with properly profiled substrates of higher permittivity, along with smaller radiation losses. These probes were found competitive and even advantageous in comparison with birdcage coils because of better stability of their Q-factors at loading by lossy phantoms. Thus, patch-based probes offer a viable alternative to RF probes for ultrahigh field MRI.