Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on 3D Object Pose Estimation Method in Intelligent Logistics*

In this paper, we propose a 3D object pose estimation method based on the frustum point cloud for industrial picking robot to grab the express. The core of our idea is to segment the point cloud of the object which should to be pick up first, then extract the best grab plane and grab point on the object point cloud. First of all, a mature 2D detection framework Faster RCNN is used to detect the object in RGB images. Then the 2D bounding box and category of the object closest to the camera will be output. On the basis of this, the frustum point cloud data of the detected object is generated by the combination of the depth map and the projection relationship of the RGB-D camera. Then we used PointNet network to extract the feature of each point in the generated frustum point cloud. Finally, the instance segmentation of the point cloud and the subsequent extraction of the best grab plane and grab point are performed. We show results on both real dataset and synthetic dataset which we collected by ourselves, and our 3D instance segmentation network accuracy on real dataset reached 88%.
The Study of the Applicability of Machine Learning Methods Based on Decision Trees for Holter Monitoring

In this paper we investigate the possibility of using machine learning methods based on decision trees for the analysis of electrocardiograms. In present work we consider and investigate such methods as gradient boosting, random forest and extra trees because they are most suitable for solving same problems. The obtained results show us the high efficiency of the considered methods and prove the possibility of their use for automatization of the electrocardiograms analysis.
Hybrid MPPT Controlled Solar/Wind Power System for Pumping System

This paper proposes a hybrid design for water pumping power system using solar Photovoltaic (PV) panels and wind turbines. The benefit of the proposed system is that is can be used to supply water to far remote areas and rural zones that suffer from lack of water due to inadequate rainy days. The system has the following primary components: voltage regulator block, battery bank, three-phase AC to DC rectifier, three-phase transformer, DC electric motor, and water pump. The solar power system has Maximum Power Point Tracking (MPPT) system to obtain maximum power from the photovoltaic panels. Solar irradiance was simulated using random values. The hybrid system will be simulated using SIMULINK to show the design performance.
Quality Assurance Algorithms for the Isocenter, FOV, MLC Leakage, Dynamic MLC Banks Position and Leaf Position Measurements

This article proposes a methodology for determining the dose distribution quality assurance resulting from deviations of the mechanical parameters of medical linear electron accelerators and identified during periodic quality control. The features of determining a representative patient and creating phantoms needed to analyze the detected deviations in the absorbed dose taking into account the parameters of tumors and risk organs are discussed. It is shown that the use of the technique allows us to express the results of periodic quality control of the mechanical characteristics of a linear accelerator in units of absorbed dose. Thus, most accurately assess the possibility of the clinical use of this apparatus for patient irradiation, as well as reduce the dose distribution errors.
Understanding Mid-Latitude Jet Variability and Change using Rossby Wave Chromatography: Poleward Shifted Jets in Response to External Forcing

Rossby Wave Chromatography (RWC) is implemented in a linearized barotropic model as a tool to understand the response of the mid-latitude jet to external forcing. Given the background zonal-mean flow and the space-time structure of the baroclinic wave activity source, RWC calculates the space-time structure of the upper tropospheric eddy momentum fluxes. RWC is used to diagnose and understand the poleward shift of the jet in an idealized GCM using the convergence of the vertical EP flux in the upper troposphere as the wave activity source. The poleward shifted jet is maintained via a selective “reflecting level” on the poleward flank of jet: for a given wavenumber, low phase speed waves are reflected but high phase speed waves are absorbed at the critical level on the poleward flank of jet. When the zonal-mean zonal wind increases on the poleward flank of the jet, a wider range of poleward propagating waves encounter a reflecting level instead of a critical level on the poleward flank. The increased wave reflection leads to increased equatorward propagating waves (and therefore poleward momentum flux) across the jet. Increases in wave phase speeds directly oppose the poleward shift because, in addition to the well-recognized effect of phase speed on wave dissipation in the subtropics, increased phase speeds imply more wave dissipation rather than reflection on the poleward flank via the selective reflecting level.
Airpath prediction model and predictive control of turbocharged gasoline engine

摘要:针对涡轮增压汽油机气路系统中节气门与废气旁通阀动力学耦合、机理建模复杂的问题,本文提出基于神经网 络模型的气路系统预测控制方法,实现了节气门与废气旁通阀的协调控制. 首先,针对涡轮增压汽油机气路系统map与 机理混合描述的特性,利用系统的输入输出数据,采用反向传播神经网络(back propagation neural network, BPNN)训 练得到一个非线性气路模型;其次,基于泰勒展开式对预测模型进行线性化,并对模型的精度进行了验证,进而利用该 模型预测系统的未来动态;然后,在考虑系统存在输入约束的条件下,设计了一个线性模型预测控制器对节气门与废气 旁通阀进行协调控制,实现了进气歧管压力和升压的跟踪控制进而满足发动机的扭矩需求;最后,通过离线仿真和基 于dSPACE的快速原型实验(rapid control prototyping, RCP)验证了控制系统的有效性和实时性. 关键词: 涡轮增压汽油机控制;模型预测控制;预测模型; BP神经网络 中图分类号: TP273 文献标识码: A
An Optimal Algorithm of Time Resource for Multi-Target Tracking under Active Oppressive Jamming

Phased-Array Radar (PAR) beam pointing is flexible, enabling rapid scanning without inertia, showing obvious advantages in dealing with complex electromagnetic environments and multitasking. In this paper, the multi-target tracking time resource optimal allocation of PAR under active oppressive jamming is studied. The Bayesian Cramer-Rao Lower Bound (BCRLB) of target location estimation is used as the allocation criterion, and a resource allocation algorithm is proposed. This algorithm can track more targets and improve tracking performance at the same time. Numerical simulations lastly validate the effectiveness of the proposed algorithm.
RRP: Reinforced Routing Policy Architecture for MANET Routing

In this paper we present a novel architecture for obtaining a routing scheme for mobile ad-hoc networks (MANETs). The goal as always in routing is obtaining a well-suited routing table for maximization of the message completion ratio (MCR) while keeping the delay low. We have designed a system architecture that uses an extended routing table and any side information available to the node. Side information can come in the form of physical layer indications (SNR, RSSI, etc.) or congestion (e.g. output queues length) for example. We modeled a highly dynamic MANET environment as a Markov Decision Process (MDP) and deployed reinforcement learning (RL) techniques from training database augmentation, hybrid reward, adaptive learning rate and assisted learning methodology (setting bounds on the learning process and outcome). The resulting routing policy has a probabilistic element regarding transmission to every next-hop option and it out-performs current policies both in different network loads and different noise environments i.e. the learned policy has higher MCR and almost the same delay as previously known algorithms.
Maximum Power Point Tracking Technique Using Combined Perturb & Observe and Owl Search Algorithms

Maximum power point tracking (MPPT) techniques are employed to improve the efficiency of photovoltaic (PV) systems by maximizing the amount of power generated from the PV modules. The tracking technique becomes more challenging under non-steady irradiance and temperature conditions. Perturb and Observe (P&O) algorithm is one of the widely used tracking techniques. However, P&O tracking algorithm suffers from some drawbacks such as sustained oscillation around the MPP. This paper presents a new technique to track the MPP under steady and non-steady irradiation and temperature conditions. The proposed technique combines the P&O method with the Owl Search Algorithm (OSA). The proposed MPPT technique is simulated using Matlab/Simulink environment. Three case studies including start-up and step variation of irradiation and temperature are investigated. The proposed technique is compared with P&O technique. The simulation results show that the developed technique presents fast response and efficient maximum power tracking.
Microspike Array Electrode with Flexible Backing for Biosignal Monitoring

Interest in continuous biosignal monitoring has increased in recent years with the increase in popularity of wearable devices and smartphones. The electrode is a key component for biosignal recordings, but conventional wet electrodes are not suitable as they require skin preparation and the application of a conductive gel. One proposed solution for this problem is the microspike or microneedle electrodes as they have the potential of acquire spontaneous small amplitude signals, but they require complex and expensive fabrication methods. The present work proposes a simple fabrication method and design of a dry microspike array electrode with flexible backing made of low-cost conductive polymers and demonstrates EEG and ECG recording.
A decentralized social networking architecture enhanced by blockchain

The privacy issues become a major problem that should be resolved for the existing centralized online social networks, which have prompted researchers to consider the decentralization framework for online social networks. In this paper, we propose a novel decentralized social networking architecture enhanced by blockchain technology. We use a sharding framework to increase the system scalability, a blockchain system to ensure the data integrity and consistency, a reputation-based authority control method to improve the system security.
Battery State of Charge Balancing with Accurate Power Sharing in DC Microgrid

This paper proposes a distributed control method to achieve state of charge (SoC) balancing among the distributed battery energy units (BEUs) in a DC microgrid based on accurate power sharing. The distributed control algorithm estimates the average SoC of all BEUs by means of consensus algorithm, and voltage regulation is determined by comparing the per unit power of BEU with the those of the neighbors. At the same time, the estimated average SoC and the voltage regulation are used to implement accurate power sharing according to the SoC levels of BEUs to achieve the SoC balancing regardless of the line resistance difference. Moreover, the control algorithm also restores the DC voltage level to the nominal value for stable operation. The effectiveness of the proposed method is proved by simulation.
Improving Corporate Secretary Productivity using Robotic Process Automation

Robotic Process Automation (RPA) has been introduced as a solution to automate highly structured business and operational processes that involve extensive labor and high error rate. It has been implemented in various business processes, including finance, supply chain and human resources, to improve the companies’ overall productivity. In this paper, we aim to develop an RPA system that supports Corporate Service Providers (CSP) in Singapore with their annual compliance processes for its clients and their ad-hoc requests or inquiries from its clients. These processes are highly structured and required in-depth understanding of several regulatory requirements in respect of clients’ compliance affairs which are heavily regulated by relevant government agencies. The proposed RPA system aims to reduce manual interaction with clients which has administrative in nature by having the system guiding the clients to follow through the necessary actions in fulfilling their requests. It is also designed to manage statutory record in response to various requests by the clients which may or may not require the record to be updated. We implemented this RPA system in two CSPs in Singapore for six months. Based on our evaluation, the RPA system is able to significantly improve the CSP’s productivity by over 1,051% for annual compliance processes and over 1,321% for ad-hoc client’s requests/inquiries as compared to manual processes. To complement the evaluation results, the managerial impacts for implementing this RPA system are also reviewed.
Motor Imagery based Multivariate EEG Signal Classification for Brain Controlled Interface Applications

Brain computer interface (BCI) can be defined as a pathway that enables human brain to communicate and voluntarily command an external device and generate output instead of depending upon peripheral nerves and muscular movements. Achieving maximum classification accuracy is the greatest challenge in developing a BCI system to correctly interpret the brain signals. This paper aims at investigating various classification algorithms in combination with different pre-processing techniques and comparing their results for maximum classification accuracy. Independent component analysis (ICA), principal component analysis (PCA) and notch filters are used for artifact removal, dimension reduction and noise cancellation, respectively. Left and right hand movements were recorded from the scalp using non-invasive electrodes. Fine KNN, with independent components as feature, gives highest classification accuracy in comparison with various classification techniques used in this research.
Analysis of Spectrum Requirements for Autonomous Driving Using SINR Probability Distributions

With the development of fifth generation of wireless communication technologies that meet the requirements of high reliability and low latency, autonomous driving is getting closer to reality. In recent years, as interest in autonomous driving has greatly increased, an international consensus has emerged that a broadband spectrum is required for autonomous driving. However, despite this attention, research has not proposed how to estimate spectrum requirements for autonomous driving to ensures quality of service for all users in the cell. Thus, in this letter, we propose a methodology of estimating spectrum requirements based on signal-to-interference-plus-noise-ratio probability distributions. In addition, a practical environment with a traffic model is set up with data analysis for autonomous driving. Through these processes, spectrum requirements for autonomous driving are estimated.
Design of an Area Efficient and High-gain CMOS Instrumentation Amplifier for VLSI Applications

In the present work, a new and improved CMOS instrumentation amplifier has been discussed for VLSI application. Low power differential amplifier and operational amplifier have also been designed here. Instrumentation amplifier has generally been preferred due to its easy gain changeability process. The designed CMOS instrumentation amplifier revealed satisfactory results in terms of gain, common mode rejection (CMRR), power consumption. On analysis, the proposed CMOS instrumentation amplifier shows a gain of 95.59 dB, CMRR of 108.4dB. On the other hand, Gain bandwidth is measured to be 3.95 MHz and power consumption of 4.85mW. The layout of the proposed instrumentation amplifier occupies an area of 34.74um*37.5um. The proposed circuit has been tested at 1.8V and the simulation has been carried out with the help of cadence analog design environment with UMC 90nm technology.
Horizontal advection critical for maintaining an Antarctic biological hotspot

Along the Western Antarctic Peninsula, ecological hotspots are spatially coherent with submarine canyons and have persisted for millennia. Palmer Deep Canyon, a representative hotspot, is known to have higher phytoplankton biomass than the surrounding non-canyon regions. The physical mechanisms that maintain and deliver phytoplankton and Antarctic krill biomass, potentially increasing prey availability to predators, are not well known. To better understand these important mechanisms, we deployed a purpose built integrated polar observatory consisting of high frequency radar, coordinated gliders, and moorings. The gliders identified a depth dependent influence of the underlying canyon bathymetry on physical and biological variability. A series of simulated particle release experiments in the HF radar current maps were used to estimate surface residence time and connectivity across the canyon. With an average residence time of 2 days, our analysis indicates that the elevated phytoplankton biomass over the central canyon is transported into and out of the hotspot on time scales much shorter than the observed phytoplankton growth rate, suggesting that the canyon may not act as an incubator of phytoplankton productivity as previously suggested. It may instead serve more as a conveyor belt of phytoplankton biomass produced elsewhere, continually replenishing the phytoplankton biomass for the local krill community, which in turn supports numerous top predators.
Evaluation and Benchmarking of Singularity MPI containers on EU Research e-Infrastructure

Linux Containers with the build-once run-anywhere principle have gained huge attention in the research community where portability and reproducibility are key concerns. Unlike virtual machines (VMs), containers run the underlying host OS kernel. The container filesystem can include all necessary non-default prerequisites to run the container application at unaltered performance. For this reason, containers are popular in HPC for use with parallel/MPI applications. Some use cases include also abstraction layers, e.g. MPI applications require matching of MPI version between the host and the container, and/or GPU applications require the underlying GPU drivers to be installed within the container filesystem. In short, containers can only abstract what is above the OS kernel, not below. Consequently, portability is not completely granted. This paper presents the experience of PRACE (Partnership for Advanced Computer in Europe) in supporting Singularity containers on HPC clusters and provides notes about possible approaches for deploying MPI applications using different use cases. Performance comparison between bare metal and container executions are also provided, showing a negligible overhead in the container execution.
Semi-Supervised Structured Sparse Graph Data Classification

Face classification has been developed for many years with the aim of dividing the face image into the identity of the person to whom they belong. The existing classification methods still have some problems such as the classifier is difficult to conform to the multi-level graph model and the data in the real scene rarely has a valid label. In this paper, a semi-supervised structured sparse graph data classification method for face feature extraction is proposed. The method of transforming graph data into uniform length vector is used to construct a semi-supervised method using the manifold that preserves the global data structure and local data structure. Sparse graph data classifier was utilized in which sparse learning automatically obtains the connection relationship between the label data vector and the unlabeled data vector and its weight with the L2 norm to control the model. Finally it realizes the sparse representation of the semi-supervised data. Experiments based on standard face datasets show that this semi-supervised sparse graph data
Dermatologic Hyperspectral Imaging System for Skin Cancer Diagnosis Assistance

This paper presents the development of a dermatological acquisition system based on hyperspectral (HS) imaging for the assistance in the diagnosis of pigmented skin lesions (PSLs). The developed system is able to capture HS images of 50×50 pixels and 125 spectral bands in the VNIR (Visual and Near Infrared) region between 450 and 950 nm, using a cold light halogen illumination device. The system is able to capture images of a size of 12×12 mm in less than 1 second. Employing this system, a preliminary database of 49 HS images of PSLs from 36 patients was generated. The data was labeled in four different classes and classified using a supervised machine learning method optimized by means of a genetic algorithm. The results obtained in these preliminary experiments demonstrate the potential of the developed system to perform a rapid and accurate assistance in the skin cancer diagnosis task during clinical routine practice.
Optimization of Control Strategy for Dual-motor Coupling Propulsion System Based on Dynamic Programming Method*

The configuration of single-motor direct drive or single-motor coupling with a multi-speed transmission adopted by most domestic battery electric buses has problems such as low overall efficiency and power interruption during shifting. In this paper, a dual-motor coupling propulsion system is proposed for high-performance battery electric buses. Based on the principle of minimum demand electric power, the threshold value of switching between different drive modes of electric drive system is established. Based on the dynamic programming method, the torque distribution control strategy of the electric drive system is optimized in certain drive cycle. Compared with that used the original proportion control strategy, the overall efficiency of the electric drive system is improved, and the power consumption is reduced by 13.6%.
Combining Deep Deterministic Policy Gradient with Cross-Entropy Method

This paper proposes a deep reinforcement learning algorithm for solving robotic tasks, such as grasping objects. We propose in this paper a combination of cross-entropy optimization (CE) with deep deterministic policy gradient (DDPG). More specifically, where in the CE method, we first sample from a Gaussian distribution with zero as its initial mean, we now set the initial mean to DDPG’s output instead. The resulting algorithm is referred to as the DDPG-CE method. Next, to negate the effects of bad samples, we improve on DDPG-CE by substituting the CE component with a weighted CE method, resulting in the DDPG-WCE algorithm. Experiments show that DDPG-WCE achieves a higher success rate on grasping previously unseen objects, than other approaches, such as supervised learning, DDPG, CE, and DDPG-CE.
Clinic effect of Chinese medicine Xueshuantong injection treatment in clinnic diabetic nephropathy

Objective To discuss the efficacy of Xueshuantong injection for clinnic diabetic nephropathy(DN). Methods 30 patients with DN were randomized into two groups.15 patients in control group were treated with controlling plasma glucose with insulin and conventional therapy;15 patients in treatment group also extrally received Xueshuantong injection.Both groups were treated for 2 weeks. Rennal function,blood fat and 24-hours urine protein were detected. Results Total effective rate was 80.00%in treatment group and 53.33% in control group.As compared with control group, 24-hours urine protein,blood fat and renal function in treatment group were improved significantly(P0.05). Conclusion Xueshuantong injection may improve DN.