Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Impact of Innovation Ability on the Radiation and Leading Role of Regional Central City: Take the Beijing-Tianjin-Hebei urban agglomeration as an example

By constructing an innovation capability evaluation index system, we comprehensively measure the innovation capabilities of cities in Beijing- Tianjin-Hebei Urban Agglomeration. Based on the gravity model, breakpoint model and field intensity model, we analyze the impact of innovation capability on the radiation effect of the central city. The main conclusions are as follows: firstly, it is found that the innovation ability of each city in the urban agglomeration has been enhanced, and the regional collaborative innovation has been enhanced significantly. Secondly, the innovation radiation of Beijing has been promoted gradually, and the radiation scope has been strengthened continuously. The innovation ability has obvious positive promotion function to the radiation effect of Beijing.
Incremental feedback learning mechanism for highly efficient automatic image segmentation with feature coupling

Segmentation of images is a pre-requisite for all modern high efficiency image processing system. In order to perform this task, various application specific algorithms are designed and deployed by image processing experts. These systems work on a context-specific mode, wherein all segmentation outputs are restricted by context of images for which the system is trained. In order to deploy these systems to other domains, complex tuning and optimization operations are needed. This reduces applicability of these system models for real time use cases, where general purpose segmentation methods are needed. These use cases include but are not limited to, scene classification, satellite image classification, yield prediction, traffic detection, etc. Moreover, general purpose image segmentation models work effectively only under a pre-set types of application scenarios, and need to be constantly trained in order to improve their applicability. Retraining these systems increases computational costs, and requires large training and testing delays. In order to remove these drawbacks, in this text an incremental feedback learning mechanism with feature coupling is proposed. The proposed model uses a wide variety of image segmentation methods that analyze colour, texture & shape information; and map it with relevant image features. These features are traced along with segmentation quality metrics like peak signal to noise ratio (PSNR), figure of merit (FOM), minimum mean squared error (MMSE), and probabilistic random index (PRI) in order to evaluate the best segmentation algorithm. These features are classified using an ensemble classification model for selection of the most efficient segmentation method that maximizes PSNR, & PRI while minimizing MMSE. Parametric evaluation suggests that the proposed model is able to improve segmentation accuracy by 8%, and reduce false alarm rate by 15% when compared with standard automatic segmentation models.
Evaluation of the geometrical properties distribution along the human ribs using different X‐ray imaging methods

This paper presents methods to investigate the dependence of the CT images resolution to the rib geometrical properties and the rib mechanical behavior during antero‐posterior bending. This study suggests that clinical‐CT images may be sufficient to create proper rib cage geometry. Post Mortem Human Surrogates (PMHS) ribs were harvested and tested under antero‐posterior bending. After the tests, 34 rib slices were cut from the posterior, lateral and anterior aspects. The sections were imaged with micro‐ and clinical‐CT, and their geometrical parameters were compared. The results showed that clinical‐CT overestimates the overall cross-sectional area by around 8%, cortical area by 40% and moments of inertia by 50%. A numerical scheme was developed to correct the measurements obtained from the clinical‐CT using the information gathered locally from micro‐CT. A parametric study was carried out: finite element models for the antero‐posterior bending tests were created based on the variations in geometrical parameters defined from the CT analysis. It was found that the FE model’s mechanical response was greatly sensitive to the image modality. The method presented in this study introduces a new approach that combines clinical‐CT images with the local measurements from micro‐CT. This combination was found to be effective in reducing the disparity between the experimental and numerical results.
Interleaved DC-DC boost converter in DC distribution fault detection method using Artificial Neural Networks

This paper proposes a fault detection method of the interleaved bi-directional DC-DC boost converter using Artificial Neural Networks (ANN). In the proposed method, when open-switch faults occur, fault detection is performed using the gating signal and the inductor current slope. This method can compensate for the delay time, and detect the fault fast within 2-sampling time in real-time. Through the ANN, fault detection is possible without additional circuits or complex algorithms, and training data is composed of integers, errors can be reduced. The proposed method is verified by PSIM simulation.
Influential Factors on Total Revenue of New Product: A Case Study of Smartfood Company Based on Mathematical Statistics Analysis

COVID-19's emergence has caused an impact on a large number of manufacturers and industries. While some past research is no longer time-efficient, as an emerging industry, the healthy food market research resources are not sufficient. This study aims to explore what factors could affect the total revenue of the new product. In order to find the best results, this paper reviewed previous studies and, on the basis of literature review, adopted a regression model to test the data. The data used in this paper are all provided by Smartfood Company. Due to the small number of data samples, this paper does not clean the raw data. Through regression analysis of the data, this paper builds a linear regression model and identifies the factors that have a significant impact on the total revenue of healthy food form Smartfood Company. The final results show that the Smartfood Co.'s revenue would be influenced by four explanatory to varying degrees, including price, advertising, product location in stores, and store volume. This finding basically consists of the hypothesis and the previous research, which also proves the reliability of this conclusion for the side. The research results of this paper can provide new ideas for the sales strategy of Smartfood Co.
Poster: A Methodology for Semi-Automated CAN Bus Reverse Engineering

Semi-automated Controller Area Network (CAN) reverse engineering has been shown to provide decoding accuracy comparable to the manual approach, while reducing the time required to decode signals. However, current approaches are invasive, as they make use of diagnostic messages injected through the On-Board Diagnostics (OBD-II) port and often require a high amount of non-CAN external data. In this work, we present a non-invasive universal methodology for semi-automated CAN bus reverse engineering, which is based on the taxonomy of CAN signals. The data collection is simplified and its time reduced from the current standard of up to an hour to few minutes. A mean recall of around 80% is obtained.
Analysis of the Value Equation of ROBAM Appliances Co., Ltd.'s Domestic Supply Chain in China

With the growth of economic globalization, supply chain management (SCM) has become an important strategy for contemporary enterprises to maintain and enhance their competitive advantages. As a world-renowned manufacturer of kitchen appliances, ROBAM has experienced more than 30 years of development, and the core of the company's strategy has undergone tremendous changes. This paper constructs a value equation model of ROBAM supply chain management through field research, interviews, and information retrieval. The current status of ROBAM supply chain management is examined, including cost, working capital, asset utilization, customer satisfaction, and sustainability. For the sake of better illustration, matrix and mind map are utilized to present the evolution of financial data. For the sake of data processing, Excel is utilized to analysis and visualize the results. According to the model evaluation, the most important parameter is cost because the procurement and R&D costs can be significantly reduced. Inventory as working capital is also vital for ROBAM due to the problem of high inventory fluctuations. Based on the analysis, suggestions and strategies are proposed to improve its supply chain management and remain competitiveness correspondingly. These results shed light for SCM improvements of similar types of corporations.
"Pretending to be Okay in a Sad Voice": Social Robot’s Usage of Verbal and Nonverbal Cue Combination and its Effect on Human Empathy and Behavior Inducement*

Inducing a user’s behavior through social interaction is a goal that a social robot aims to achieve. It has been argued that empathy has a strong effect on behavior inducement. In human-human interaction, it has been verified that the influence of a nonverbal cue on empathy outweighs that of a verbal cue when those are used in a combined way. The objectives of this study are to explore if such outweighing effect of nonverbal cues is maintained in human-robot interaction (HRI) and to investigate the mechanism of communication cues’ effects by analyzing the mediation structure with the following mediator variables: perceived emotion, perceived intentionality, perceived malfunction, and empathy inducement. To this end, we conducted 2 (verbal type: positive verbal cue vs. negative verbal cue) × 2 (nonverbal type: positive nonverbal cue vs. negative nonverbal cue) within-participant experiment (N = 48). The experiment created a situation in which the social robot was harshly criticized during a conversation. The analysis of experiment results showed the outweighing effect of a nonverbal cue was maintained. When a nonverbal cue conveyed a negative, situation-accordant emotion, it had a decisive effect on perceived emotion, empathy, and behavior inducement. In contrast, a verbal cue induced participants’ empathy and behavior when it conveyed a positive, situation-discordant emotion. This inconsistency between verbal cue and nonverbal cue made the combination of positive verbal cue and negative nonverbal cue have the strongest effect. It implies that participants had different expectations for each of the two communication cues, just as they did in social interactions with human beings. It implies that participants might have applied normative expectations for social interaction with human beings to the social robot.
Semantically Informed Next Best View Planning for Autonomous Aerial 3D Reconstruction

To capture the geometry of an object by an autonomous system, next best view (NBV) planning can be used to determine the path a robot will take. However, current NBV planning algorithms do not distinguish between objects that need to be mapped and everything else in the environment; leading to inefficient search strategies. In this paper we present a novel approach for NBV planning that accounts for the importance of objects in the environment to inform navigation. Using weighted entropy to encode object utilities computed via semantic segmentation, we evaluate our approach over a set of virtual Gazebo environments comparable to construction scales. Our results show that using semantic information reduces the time required to capture a target object by at least 40 percent.
Neurointerface implemented with Oscillator Motifs

In this work, we present a definition of a neurointerface architecture combined from two main parts (1) neuroport (a hardware device) that implements a neuro protocol, generated and managed by a (2) neuroterminal (a software). The proposed architecture was created by analogy with OSI network architecture. We also present the neuroterminal as an oscillator motif real-time neurosimulation and results of the comparison of a bio-plausible motor pattern generated by oscillator motifs with square pulses of 20 – 40 Hz used as the neuro protocol for the output neuroport and measured their discomfort rate and efficacy according to an angle of subject fingers deflection. We determined that the most effective is the five oscillator motifs generated pattern for a median nerve stimulation, whereas for a muscle simulation 20 and 40 Hz are more effective. We indicate that the oscillator motif generated pattern feels more natural than square pulses 20 – 40 Hz, which feel like a spasm.
Origami Logic Gates for Printable Robots

Origami robots–often called "printable" robots– created using folding processes have gained extensive attention due to their potential for rapid and accessible design and fabrication through simple structures with complex functionalities. However, almost all origami robots require conventional rigid electronics for control, which may hinder the integration and restrict the potential of these origami systems. Here we introduce origami logic gates that can be built through folding. The major enabling technology is a bistable switch that can switch between two different circuits to control the electrical flow. Based on the origami switch, we develop NOT, AND, and OR logic gates (showing functional completeness) and demonstrate these logic gates through sufficiently powering low-current LEDs. These logic gates are fabricated using cut-and-fold manufacturing and offer a potential way of integrating logic functions directly into origami machines without electronics.
Inductive coupler for contactless power transmission

To provide power to a moving vehicle without using contacts, a transformer is presented, consisting of a fixed primary winding inductively coupled to a moving secondary winding. The primary winding along the whole trajectory of motion is configured such that its magnetic stray field and its self inductance are limited. This winding transfers power to the moving coupler containing the yoke and the secondary winding. The secondary winding is a resonant circuit, with a rectifier and the motor of the vehicle as load. To describe the energy transmission system, a numerical model is developed that combines an electrical circuit with a 2D nonlinear finite element model. This model is used for an optimisation of the design. For the optimised configuration, an experimental setup is built to validate the model by measurements. A sensitivity analysis is carried out concerning the primary current, the supply frequency, the position of the secondary winding relative to the primary winding and the number of vehicles coupled with the primary winding.
A Novel Switching Method for All-speed Sensorless Control Strategy Based on Aviation Motor

All-speed sensorless control method on aviation motor has been investigated in recent years due to the strict operation request of More Electric Aircraft (MEA). Generally, I/F startup strategy and back-EMF based method are combined with a linear current reduction (LCR) switching method to realize all-speed sensorless control on PMSM. Nevertheless, conventional LCR method has bad transition performance under no-load and light-load conditions. Therefore, a linear angel reduction (LAR) switching method is proposed in this paper to smooth the transition process between I/F strategy and SMO algorism. The current in LAR method declines in a specific regulation which causes the angel to reduce in a constant rate. LAR method can be used in speed stationary and speed variation circumstance, furthermore it doesn't need to change parameters along with load variation. Compared to LCR method, LAR method eliminates the angel jumping problem in switching process. Consequently, it not only suppresses the current ripple but also reduces the speed oscillations significantly. Comparison simulation results of LCR method and LAR method are presented to verify the feasibility and effectiveness of the proposed switching method.
Robust Pose Estimation Based on Normalized Information Distance

Dense image alignment works by minimizing the photometric error of two images since it is assumed that the illumination changes between images close in time remain the same—this is what is called the brightness constancy assumption. However, this assumption does not hold with long-term maps since illumination changes continually from day to day (morning, afternoon, evening) and is dependent on certain external conditions like weather or even seasons. In this work, we present an image registration algorithm based on the Normalized Information Distance (NID) that is shown to be robust to extreme illumination changes comparing to the traditional direct methods. The pose is estimated by minimizing the NID function with the help of the nonlinear least square optimization library G2O. We share our source code 1 (CPU and GPU version) for the benefit of the community, which can be a strong basis for future tracking and mapping system based on NID.
Research on the Relationships between Current Harmonics and Torque Harmonics of IPMSM

In order to study the inherent relationships between current harmonics and torque harmonics, the finite element (FE) method of distributed winding interior permanent magnet synchronous motor (IPMSM) are carried out in this paper. The high-order current harmonics related to carrier frequency are focused. According to the analysis results, the numerical relationships between the components of current harmonic and the components of torque harmonic are determined. Then, the corresponding torque harmonics, generated by the current harmonics, can be calculated based on the numerical relationships between the components. The calculated torque results are in good agreement with the results of FE method. It shows that the numerical relationships can accurately restore the internal relationships between current harmonics and torque harmonics.
Design of a 300W axial flux motor for an electric bike

The motor of the electric bike is crucial to the overall performance of the bike. A lightweight but powerful and reliable motor is required for electric vehicles. A Brushless permanent magnet motor is selected to meet those requirements. Axial-flux machine topology and Halbach-array are utilized in the motor design to further increase the power density. The tooth-tips in the stator core are removed to simplify the manufacturing process and reduce core weight. The rotor position is detected by the utilization of Hall sensors. Electromagnetic and thermal analyses are performed with three-dimensional finite element analysis to shows the advantage of such topology in an electric bike. A prototype is manufactured and tested at rated conditions to validate analysis results.
LDPC coded unitary space-time modulated OFDM system in broadband mobile channel

A unitary space-time modulated orthogonal frequency division multiplexing (OFDM) system using low density parity check (LDPC) code for increasing the system performance is proposed. The proposed system can obtain space-time diversity using unitary space-time modulation without linear operation like space-time block code (STBC), and large coding gain using LDPC code.
Multi-robot Task Assignment for Aerial Tracking with Viewpoint Constraints

We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles. We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint. Given desired shot parameters, the high-level planner uses a visibility heuristic to predict good times for capturing each shot and uses an Integer Linear Program to compute drone assignments. An online Model Predictive Control algorithm uses the assignments as reference to capture the shots. The algorithm is validated in hardware with a pair of drones and a remote controlled car.
Multi-modal Sensor and Data Processing for Comprehensive Skin Evaluation

This paper presents a novel skin testing device, called the skin sensor, which can realize multi-modal sensing of optical and mechanical skin properties. The sensor structure and operating cycle are described in detail. Based on the calibration result and physical model of the skin sensor, the algorithms to extract optical and mechanical skin properties from skin sensor data are established, which are simple, efficient, and moreover, self-decoupling for different sensing modes. Experiments are conducted with testee to verify the comprehensive skin evaluation ability of the skin sensor, which shows positive results. This skin sensor has great potential in cosmetic usage due to its portability and strong skin state monitoring ability.
Towards Intelligent Fruit Picking with In-hand Sensing

Studies have shown that picking techniques play an important role in determining fruit quality at harvest (e.g. bruising, stem retention, etc). When picking fruit such as apples and pears, professional pickers use active perception, incorporating both visual and tactile input about fruit orientation, stem location, and the fruit’s immediate surroundings. This combination of tactile, visual, and force feedback is what enables human workers to execute dynamic movements that quickly and efficiently remove fruit from the tree without damage. However, much of the prior work on robotic fruit picking has formulated the harvesting problem as a position-control problem, using visual feedback for closed-loop end-effector placement while disregarding feedback on physical contact. As a first step towards more intelligent fruit picking — combining proprioception, localized sensing, and observed forces — we have developed a custom end-effector with multiple in-hand sensors, including tactile sensors on the fingertips. This paper presents the mechatronic design of the device as well as results from multiple outdoor picking trials with a Honeycrisp apple tree. Preliminary results show that, with multi-modal sensing, fruit slip, fruit separation from the tree, and fruit release from the hand can be detected.
Quadrupedal template model for parametric stability analysis of trotting gaits

Simple template models have proven useful for understanding the underlying dynamics of legged locomotion. The most common one, the SLIP model, considers the legs as linear springs with constant stiffness, and it explains well the radial dynamics of the legs. However, in order to study the influence of the leg swing dynamics and leg segmentation on gait stability, more complex models are required. This paper introduces a novel template model for quadrupedal gait, which considers these additional aspects. The dynamic behavior of the model is analyzed via numerical simulation, using a continuation approach. By conducting a parametric analysis on the trotting gait and analyzing its stability, we identify the influence of the main model parameters, leading to marginally unstable limit cycles. These numerical results are applicable to the design of more efficient elastic quadrupedal robots.