Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cloud-edge Collaborative Framework for Computing Tasks Based on Load Forecasting and Resource Adaptive Allocation

In recent years, cloud-edge collaboration has attracted extensive attention and research. Robot target tracking is a common application scenario for collaborative computing, which usually involves visual location, target analysis, route planning, attitude control and other task types with different computational complexities and real-time requirements. Given different computing capabilities, service loads, communication overheads, area crossing of different nodes at different time, the problem of how to allocate various services of the robot among the local, edge servers and cloud servers effectively according to the optimization objectives involves complex cloud edge cooperation strategy in the process of robot target tracking. To solve this problem, this paper proposes a cloud-edge collaborative framework for computing tasks based on load forecasting and resource adaptive allocation, which can efficiently allocate tasks of different types and complexities according to the current relative position of the robot with the servers, the computing resources and current loads of edge servers and cloud server. A large number of experiments show that this method can effectively shortens the average response time of various services and improves the service quality compared with traditional methods.
Underwater fish detection in sonar image based on an improved Faster RCNN

For the efficient detection of underwater fish, this paper proposes a target detection algorithm based on the improved Faster region-based convolutional neural network (iFaster RCNN). On one hand, the proposed algorithm combines feature pyramid network (FPN) with the original Faster RCNN for solving the multi-scale problem in target detection. On the other hand, in order to further enhance the detection accuracy and increase detection speed, Distance-Intersection-over-Union (DIoU) is used to replace Intersection-over-Union (IoU). Experimental results show that, with FPN and DIoU, iFaster RCNN has higher detection accuracy for underwater fish. For comparison purposes, VGG16, MobileNetV2, and ResNet50 netwoks are used as the backbone feature extraction networks of iFaster RCNN. Comparative results prove that ResNet50 performs better than the other two netwoks.
A review on challenges for the dissemination of battery electric vehicles in Brazilian market

This paper presents a survey on the main challenges to disseminating battery electric vehicles in the Brazilian power sector and conserving a sustainable position in the market. This research addresses the analysis of the advantages, challenges, and obstacles of the current context in the country, through the study of international public policies and the Brazilian regulatory scenario, given the observation of the significant difference between the introduction of the battery electric vehicle in the world market and Brazil. This paper presents an exploratory meaning which provides the challenges for the spread of electromobility in Brazil.
Matching deformed Delaunay triangulations

This paper describes a Bayesian framework for matching graphs whose structure can be described in terms of triangular faces. Relational structures of this sort are ubiquitous in intermediate level computer vision, being exemplified by Delaunay graphs which represent the Voronoi tessellation of an image plane. Our matching process is realised in terms of probabilistic relaxation. The novelty of our method stems from its use of a support function specified in terms of face-units of the graphs under match. In this way we draw on more expressive constraints than is possible at the level of edge-units alone. In order to apply this new relaxation process to the matching of realistic imagery requires model of the compatibility between faces of the data and model graphs. We present a particularly simple compatibility model that is entirely devoid of free parameters. It requires only knowledge of the numbers of nodes, edges and faces in the model graph. The resulting matching scheme is evaluated on radar images and compared with its edge-based counterpart.
A Hybrid Half-bridge LCC Resonant Converter and Phase-Shift Full-bridge Converter for High Step-up Applications

An input-parallel and output-series hybrid topology of half-bridge LCC resonant converter and phase-shift full-bridge converter is proposed in this paper. The lagging-leg switches can realize Zero-Voltage-Switching (ZVS) based on the parallel resonant LCC configuration. The voltage stress on the output rectifiers can be significantly reduced by using the current driven rectifiers and dual output in series configuration. The operation principle, Steady-state characteristics analysis are presented, and the simulation is verified. The results show that all of the active switches can realize ZVS in a wide load range; ZCS is realized by secondary rectifier, which improves the efficiency; the output diode voltage stress is reduced by using a series output structure on the secondary side.
Hemodynamic Response Asymmetry of the Prefrontal Cortex During a Cognitive Load Task

Investigations of the prefrontal cortex (PFC) asymmetry have been conducted in neuroscience research during cognitive load using +electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including ive, 2-, and 3-back electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including 1-, 2-, and 3-back. The investigation results show that the asymmetry index value fluctuates as the level of memory load rises. In particular, the 1-back task's positive asymmetry index value (M = 0.2761,SD = 0.4139) suggested that left-hemisphere activity was more remarkable than right-hemisphere activation. The asymmetry index, on the other hand, revealed a negative value of (M = - 0.2105,SD= 0.4252) and (M = - 0.3665,SD = 1.2472) for the 2-back and 3-back memory tasks, respectively, indicating that the right hemisphere experienced a more significant increase in Hbo activation than the left.
P3: A task migration policy for optimal resource utilization and energy consumption

Theevolution in modern technologies like artificial intelligence, machine learning, cloud computing, edge computing, data science, etc, focuses on user perspectives like accuracy, response-time, and timeliness but at the same time consumes heavy energy due to large and fast data processing. From the system perspective, resource utilization and energy consumption are also significant design considerations. This work proposes a task migration policy for optimal core utilization and energy savings. The time taken by data analytical tasks to process the data varies, due to variations in the amount of data it analyzes in unit time. This creates variation in the core utilization due to which there exist small inactive intervals in the schedule, consuming energy. If the inactive state is known to continue for a longer duration, the core can be put into a shutdown state which effectively reduces overall energy consumption. Dynamic Procrastination (DP) is a commonly used technique to increase the inactive duration by postponing the tasks whenever possible. To further increase the inactive duration to qualify for shutting down the core, in a homogeneous multi-core (HMC) system, the jobs can be migrated to other cores. This effectively improves core utilization and reduces overall system energy without negatively affecting performance. Combining the DP and migration techniques introduces challenges like meeting deadlines, deciding upon push/pull migration, finding the number of tasks and suitable core for migration, and computation of energy consumption parameters. This paper proposes P3 (Push-Procrastinate-Pull) migration policy for the HMC system. The experimental evaluation with synthetically generated benchmark program suites shows that on an average P3reduces the overall energy by 1.2% and reduces the shutdown duration over the idle period by 2.22% over DP without migration.
Research on hole feature matching based on image restoration

Aiming at the problem that stains and strong light reduce the accuracy of light hole and threaded hole matching in the process of traditional template matching, a template matching method for body in white hole feature based on image restoration is proposed. Firstly, the patch match repair method is used to repair the image of stains and strong light, so as to eliminate the interference of the two to the matching. Then the light hole and thread are matched by combining the gray matching method of improved search strategy and the edge feature matching method respectively. Finally, the sub-pixel coordinate fitting is performed on the matching results. Experimental results show that the accuracy of matching smooth holes and threaded holes can reach 100% and 98%, and the difference between matching accuracy and commercial software matching accuracy is less than 0.3.
Teaching Reform of Visual Communication Design Specialty from the Perspective of Embedded Mode

Due to the rapid development and wide application of embedded technology, many domestic universities and vocational and technical training institutions have also set up embedded system experimental teaching courses, which makes the design and development of experimental system an urgent problem in embedded field teaching. This paper discusses the concept of embedded system, embedded teaching service, and analyzes the characteristics and types of embedded system, teaching reform. This paper analyzes the current situation of embedded curriculum in universities. The results show that when undergraduate colleges set up embedded related courses, many theoretical courses is much larger than that of experimental courses. Many colleges have reduced the number of experimental classes and let students find experiments to do by themselves.
Local Ecosystem Open Innovation Intermediaries as Key Enablers for the Development of Incumbents' Digital Technology Partnerships

Partnerships between incumbents and digital technology entrepreneurs are prone to develop innovative solutions needed by the coming “new normal” society. This paper focuses on the banking sector, which is currently facing huge challenges related to open innovation and digitalization. The qualitative approach adopted is based on observations and interviews involving actors from a given ecosystem: Luxembourg. The objective of this article is to explore the roles of innovation intermediaries in the capability development of incumbent firms’ to establish new collaborations with innovative digital technology startups. We employ the dynamic capabilities framework to tackle this issue. Our findings show the crucial complementary roles of both external and internal innovation intermediaries in enabling digital technology partnerships and developing incumbents’ relational capabilities. This paper contributes to knowledge in the field of strategic management by showing how traditional, highly structured organizations such as banks can regenerate their dynamic capabilities by being strongly engaged in their open innovation ecosystem and connected to a variety of actors from their ecosystem, especially open innovation intermediaries. For policy makers, this research suggests the deployment of means to empower these key interfaces to act in favor of their ecosystems.
Automatic Measurement of Spinous Process Angle on Ultrasound Spinal Column Image

Adolescent Idiopathic Scoliosis (AIS) is scoliosis that affects adolescents. Recently, ultrasound and Spinous Process Angle (SPA) have been a method to detect AIS. This study took images of 7 different subjects. During pre-processing process, the image quality was improved using CLAHE, Median Filter. The polynomial regression results are searched for the leftmost or rightmost positions to find the scoliosis curve. Three points are taken on the curve as measurement points from the SPA. This automatic result is compared with the gradient vector flow. The results were then evaluated using Pearson's Correlation by comparing the obtained SPA with Cobb Angle. The result for manual SPA is 0.2 and 0.25 for automatic SPA. These two results were compared resulting in Pearson's Correlation of -0.15. The automatic measurement system on ultrasound images can be an alternative to measuring the angle of scoliosis in the future.
Detecting Microatolls from Drone Images

Visual object detection has made significant progress with the advent of deep neural networks and has been extensively applied. This work reports a novel application that aims to detect individual microatolls, which are circular coral colonies, from island images captured by drones. We first describe data collection and labelling to create a microatoll detection dataset. Upon this dataset, the state-of-the-art object detectors are then evaluated for this task. To better integrate a detector with the characteristic of microatolls, we propose a modified detector called Microatoll-Net. It actively extracts features from the surrounding area of a microatoll to differentiate it from distractors to improve detection. Multiple ways to incorporate this information into the detector are designed. Experimental study shows the efficacy of the proposed Microatoll-Net, especially on the most challenging region of an island. The code and dataset will be released soon.
Cost-benefit Analysis of Virtual Power Plant for Demand Response Scenario

With the development of distributed new energy and demand response technology, Virtual Power Plant (VPP) has attracted more and more attention. The State Grid Corporation of China has started the pilot construction of VPP in Hebei and Shanghai since 2019. The responsiveness and flexibility envisioned for the VPP provide additional advantages in facing the significant new challenges of new energy consumption and peak shaving. To further promote the VPP, cost-benefit analysis is of great significance. This paper presented an cost-benefit analysis model of the VPP for demand response scenario. The proposed model was used to analyse the cost-benefit of Tengtian VPP in Shanghai based on the data of demand response transaction in 2021. Results show that even the VPP carried out demand response every day, the VPP still made a net loss of 0.86 million per year, which indicates an urgent need to improve the promotion mechanism of demand response for VPP.
Haptically perceived orientation of a planar surface is altered by tangential forces

We describe an experiment showing that addition of tangential forces can alter the haptically perceived orientation of a flat surface in a systematic fashion. Using a stylus mounted on a Phantom controlled by the GHOST software package, subjects performed single horizontal strokes along vertical surfaces at a number of orientations around the mid-sagittal plane. After each trial, subjects (who could not view their hand) judged whether the surface was rotated around the vertical axis in the clockwise or counter-clockwise direction from the sagittal plane. Psychometric curves for perceived surface orientation were measured in this way for various magnitudes of tangential forces along the stroke direction or in opposition to it. Psychometric curves shifted systematically with varying force magnitude, indicating that resistive forces tilted the perceived surface into the path of motion, while assistive forces tilted it away. More generally, the results show that perception of global surface orientation is not exclusively based on the location of the surface in space, but also on the forces encountered along the surface.
Selection of the Disruptive Nodes to Destroy Power Grid

Due to the decentralization and complexity of network topology in power grid, the key substations are easily damaged. The disaster like the North American blackout in 2003 give us a warning about the control of power grid. It is vital that the key substations or destructive nodes to destroy the power grid are recognized firstly. Hence, in this paper the several functions to determine the importance of nodes in a complex network were adopted to search for the destructive nodes in a power grid. The implementing procedure and the basic principle of different functions to select disruptive nodes are firstly introduced. Then the node data from Western United States power grid was taken as an example, in order to check and compare the effectiveness of each functions mentioned above. The results show that the selected destructive nodes by three methods are diverse from each other. The distribution of important vector by the degree method shows a step mode, while a very good continuity is obtained by the AD method.
Comparison of Semantic Segmentation Deep Learning Methods for Building Extraction

Urban planners use building extraction on satellite imagery to support government policies. However, the complex depiction of buildings in satellite imagery makes building extraction difficult. One way to extract buildings in satellite imagery is by semantic segmentation deep learning. This study aims to find a suitable deep learning semantic segmentation method by comparing the performance of UNet, UNet++, DeepLabV3, and DeepLabV3+ that combined with ResNet-101 and ResNet-50 as feature extraction algorithms and trained on two public datasets with different characteristics. UNet++ produces the highest performance for predicting both datasets, but with different feature extraction algorithms. MBD feature extraction is more suitable using ResNet-101 while AICrowd uses ResNet-50. However, if we consider time-consuming, DeepLabV3+ and UNet are more efficient for training building datasets because of consuming less time with quietly performance
Application of Constrained H∞ Control to Active Suspension Systems on Half-Car Models

This paper formulates the active suspension control problem as disturbance attenuation problem with output and control constraints. The H∞ performance is used to measure ride comfort such that more general road disturbances can be considered, while time-domain hard constraints are captured using the concept of reachable sets and state-space ellipsoids. Hence, conflicting requirements are specified separately and handled in a nature way. In the framework of Linear Matrix Inequality (LMI) optimization, constrained H∞ active suspensions are designed on half-car models with and without considering actuator dynamics. Analysis and simulation results show a promising improvement on ride comfort, while keeping suspension strokes and control inputs within bounds and ensuring a firm contact of wheels to road.
Gauss–Seidel Convergence for Operators on Hilbert Space

For invertible linear operator A on Hilbert space $\mathcal{H}$, we consider classes of splittings for A (viz., $A = A_1 + A_2 $, where $A_1 ,A_2 $ and $A_1^{ - 1} $ exist as a bounded linear operator on $\mathcal{H}$) for which $\rho (A_1^{ - 1} A_2 )$, the spectral radius of $A_1^{ - 1} A_2 $, is less than one. For example, we introduce the class of $\alpha $-splittings (Definition 3.2) which generalizes the classical notion of the lower-triangular $A_1 $ and upper-triangular $A_2 $ splitting for an $n \times n$ matrix A. Tests are presented which guarantee Gauss–Seidel convergence (i.e., $\rho (A_1^{ - 1} A_2 ) < 1$) relative to $\alpha $-splittings (cf. Theorem 3.6, Corollary 3.7, Corollary 3.8). Another class of splittings for (self-adjoint) linear operators $A = A_1 + A_2 $ is an operator-parameter generalization of the $\omega $-splitting which occurs in the matrix method of successive overrelaxation. In fact, Theorem 4.2, which establishes conditions on this splitting sufficient for Gauss–Seidel c.
Research on Practical Application and Optimization of Multi-Strategy Prediction Method Based on Machine Learning

In this paper, through the method of mathematical modeling, based on the practical application of the prediction model for research and mathematical modeling. First of all, based on literature research, this paper puts forward the basic prediction model and takes several high-precision algorithms based on statistics and machine learning as the sub-modules of the prediction model. We integrate multiple predictions provided by the sub-module and provide the final prediction through the integration. Then the prediction is regarded as a planning problem, and the index quantity of the optimal weight is introduced into the optimization objective to predict and get better results.
Convolution Neural Network (CNN) Architectures Analysis for Photovoltaic (PV) Module Defect Images Classification

Photovoltaic (PV) module is the medium to convert solar energy to electrical energy. The existence of defects in the PV module will affect the system's efficiency to generate electricity. In this work, Convolutional Neural Network (CNN) architecture is proposed in PV module defect image classification due to its capability to extract patterns and consist of several unique layers to classify images accurately. Thus, the objective of this paper is to analyze PV defect image classification using CNN architectures namely Residual Neural Network (ResNet) and Visual Geometry Group (VGG), and to identify which architectures give the best performance. In this paper, two types of ResNet architectures which are ResNet-18 and ResNet-50, and two types of VGG architectures which are VGG-16 and VGG-19 were applied. For validation and verification purposes, other performance metrics such as F1-score, sensitivity, specificity, and precision are evaluated. The result shows that VGG-19 is outperformed in terms of accuracy with a value of 92.58%, sensitivity (73.14%), and specificity (95.10%) compared to ResNet-18, ResNet-50, and VGG-16 architecture.
Teaching Content Recommendation Strategy based on Neural Network Unit Rating

With the continuous improvement of the online teaching platform, data are accumulated to provide the main basis for tailor-made teaching based on students' personality characteristics. Process assessment data and learning evaluation data are collected, a unit evaluation strategy based on the long and short-term memory neural network LSTM is given, and a recommendation strategy for students' pre-review content is given based on the evaluation strategy. The strategy is applied to the knowledge recommendation problem of the basic artificial intelligence experimental course, and the results of the strategy are analyzed and compared with the evaluation strategy results of the traditional Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The data show that the recommendation accuracy of the LSTM-based multi-attribute evaluation strategy is relatively high in recommending knowledge points of neighboring chapters, and the error is low when compared with the actual student questionnaire data. In recommending knowledge points for chapters that span a wide range of chapters, there is not much difference between the two algorithms.