bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electronic Differential Control System Based on Particle Swarm Optimization and PID Control
Authors: Yang Dongxue , Lu Huacai , Zhang Chenyan 
Published: April 2022

Abstract
For the front wheel steering, the control effect of the four-wheel hub-driven electric vehicle driven by the rear wheel is not accurate and stable. It is proposed to replace the traditional PID control system with the particle swarm algorithm to realize the online optimization of the PID regulator parameters. The simulation model of the improved system is built in MATLAB. Compared with the conventional PID control method, the results show that the improved four-wheel drive system has a smaller slip ratio between the left rear wheel and the right rear wheel, which improves the directional stability and braking efficiency of the wheel electric vehicle.
Research on Distribution Network Fault Location based on Improved Genetic Algorithm
Authors: Wang Hao , Mei Kaifeng , Zhu Chao , Zhai Chengjian , Li Meng 
Published: April 2022

Abstract
Compared with the traditional recloser fault location without communication channel, feeder automation based on FTU is in line with the development of modern distribution system. FTU plays a key role in the whole system, but the location is inaccurate due to the lack of information caused by FTU itself or communication fault. When using genetic alogrithms for distribution network fault location, the result often converges prematurely or converges to an infeasible solution due to algorithm reasons. Aiming at the above problems, an improved genetic algorithm is proposed for distribution network fault location. Through simulation analysis, it can be found that the improved genetic algorithm has high convergence and good optimal fitness, which can better solve the convergence problems of the unmodified algorithm, which is of great significance for distribution network fault location.
Research on fronthaul scheme based on DRoF
Authors: Feng Chenwei , Sun Yu , Su Youmei , Jiang Fuchun , Tao Lin , Ji Huazhi 
Published: April 2022

Abstract
With the rapid increase of global data traffic, storage and transmission of massive data is a challenging problem. Therefore, data compression is necessary. Based on DRoF, mobile fronthual has the advantages of high data rate, good connectivity and system capacity, and becomes the key part of communication system transmission. In this paper, an optimized vector quantization data compression algorithm based on DRoF system is proposed, which mainly studies OFDM baseband modulation signals. Firstly, the modulation signal is transformed by DCT. Then the optimized fast-global K-means clustering algorithm is used for quantization to overcome the problem that the original algorithm may choose noise points as the cluster center. Finally, Huffman coding is used to reduce the code length of the quantized data. Simulation results show that the proposed algorithm has good CR and EVM performance within the error tolerance range. It has a high degree of compression and signal recovery.
Research on Cooperative Control for Multi-motor System of Space Climbing Robot
Authors: Yin Yecheng , Yan Guodong , Huang Yuping , Chen Zhihong , Yu Zhiyuan , Jia Longfei 
Published: April 2022

Abstract
A system model of space climbing robot with dual power units is established and three control strategies for motors are presented in this study. The simulation results show that the robot has a certain fault tolerance for the speed synchronization of motors, and voltage difference on motors can achieve dynamic balance of power. Control strategy based on voltage synchronization, control strategy based on current synchronization and control strategy based on current following have been proposed. The simulation results show that those control strategies can effectively distribute the system power. Among them, the control strategy based on voltage synchronization is the simplest and has the best effect. It provides a reference for further research on the control method of multi-motor climbing robot.
Temporal Logic Control Synthesis for Distributed Multi-Agent Cooperative Tasking
Authors: Xu Ning , Peng Ting , Liu Dawei , Li Jie 
Published: April 2022

Abstract
In this work, we present a novel control synthesis method for cooperative multi-agent systems to fulfill a global mission given as linear temporal logic on finite traces (LTLf). The proposed method first synthesizes satisfiable global controllers for uncontrolled system, such that the global specification is met; and then, searches for a decomposable global controller among them over a maximum synchronization scheme; finally, further refining the synchronization scheme to obtain decomposed distributed controllers. To search for a decomposable global controller among the satisfiable ones, we present an informed searching algorithm based on the decomposability analysis of global controllers. A multi-UAV cooperative fire surveillance scenario is developed to illustrate the method.
Federal SNN Distillation: A Low-Communication-Cost Federated Learning Framework for Spiking Neural Networks
Authors: Liu Zhetong , Zhan Qiugang , Xie Xiurui , Wang Bingchao , Liu Guisong 
Published: April 2022

Abstract
In recent years, research on the federated spiking neural network (SNN) framework has attracted increasing attention in the area of on-chip learning for embedded devices, because of its advantages of low power consumption and privacy security. Most of the existing federated SNN frameworks are based on the classical federated learning framework -- Federated Average (FedAvg) framework, where internal communication is achieved by exchanging network parameters or gradients. However, although these frameworks take a series of methods to reduce the communication cost, the communication of frameworks still increases with the scale of the backbone network. To solve the problem, we propose a new federated SNN framework, Federal SNN distillation (FSD), whose communication is independent of the scale of the network. Through the idea of knowledge distillation, FSD replaces the network parameters or gradients with the output spikes of SNN, which greatly reduces the communication while ensuring the effect. In addition, we propose a lossless compression algorithm to further compress the binary output spikes of SNN. The proposed framework FSD is compared with the existing FedAvg frameworks on MNIST, Fashion MNIST and CIFAR10 datasets. The experiment results demonstrate that FSD communication is decreased by 1-2 orders of magnitude when reaching the same accuracy.
2D Pose Estimation Based on Deep Learning for Mobile System
Authors: Liu Yan , Wang Jingwen , Li Yujie , Li Guangwei , Tang Gaopeng 
Published: April 2022

Abstract
Pose estimation is the detection of human action poses, which is an important task of computer vision. The requirements of tasks such as unmanned driving and intelligent surveillance have also contributed to the development of human pose estimation. In this context, deep learning (DL) has a remarkable impact on human pose estimation. In this study, we investigate a pose estimation algorithm called convolutional pose machines (CPM) and implement it on a mobile platform (i.e., Android). By combining the classical CPM and the mobile deep learning framework Mobile AI Compute Engine (MACE), the model is deployed to the mobile platform to estimate the human pose directly and locally on the mobile phone without relying on the Internet. Thus, the proposed method can achieve effective performance while protecting users’ personal data. Experimental results on real-world data validate that this system has achieved the expected results of accurate recognition of common actions with a simple interface.
Research on Job-shop Scheduling Problem Based-on Improved Genetic Algorithm
Authors: Wei Bo , Qi Jiayuan , Wen Jiafu 
Published: April 2022

Abstract
Considering minimizing completion time and maximizing equipment utilization of job shop scheduling problem, an improved genetic algorithm was proposed. First, constructing a two-layer chromosome coding structure, with which the initial population quality and the diversity of the population were strengthened and improved. Next, in order to enhance the local search ability and improve the algorithm convergence speed, the population generated after selection, crossover, and mutation was divided into families, and the elite population were combined with explosive fireworks of the firework algorithm. Then, in order to prevent the algorithm from maturing prematurely, the disadvantaged groups were mutated through(by) the dynamic mutation mechanism to narrow the gap between the super individual and the average. Last, the parameters of the algorithm were optimized by orthogonal tests, and performance of the algorithm was verified by classical test examples.
Observer-based Finite-time Boundedness of Itô Stochastic Markov Jump Systems with Time-varying Delay
Authors: Liu Xiao , Liu Ningzhe , Li Yan 
Published: April 2022

Abstract
This paper is concerned about the problem of observer-based finite-time boundedness (OBFTB) of
Itô
stochastic Markov jump systems (MJSs) with external disturbances and time-varying delay. To begin, an observer-based controller (OBC) is constructed to assure finite-time boundedness (FTB) of the closed-loop system. Then, using linear matrix inequalities (LMIs), the state feedback gains and observer gains are calculated. Eventually, a numerical example is provided to demonstrate the value of the obtained results.
An MPF Based Aerial Platform Tracking Method for Road Network Targets
Authors: Yuan Jing , Chen Xin , Zhang Min , Zhang Pengfei 
Published: April 2022

Abstract
This paper presents a new algorithm to improve the tracking accuracy of the aerial mobile observation platform by using road information constraints. Combined with the road information and Mixed Particle Filter(MPF) algorithm, the Road Constrained Mixed Particle Filter(RCMPF) algorithm is proposed. RCMPF algorithm can effectively solve the problem of non-linear observation equation of aerial observation platform and realize the tracking of ground targets. The simulation results show that the algorithm improves the tracking accuracy of the target.
Detection of Chip Layered Defects Based on Dual Focus Mechanism
Authors: Chen Shuguang , Gao Jingyang , Zhao Di , Xu Pinjie , Zhang Tian 
Published: April 2022

Abstract
Chip layering defects affect the performance of chips and lead to the failure of chips, so chip layering defects detection is an important step in the quality acceptance of chip production. Chip layering defects, which are characterized by insignificant color change in defect area, small defect area and difficult localization, bring challenges to traditional detection. In recent years, deep learning has shown its powerful ability to solve complex problems in computer vision. In this paper, semantic segmentation method is used to study the problem of chip hierarchical defect detection. Dual focus mechanism first applies whiteboard network structure to identify the true hierarchical area. Afterwards the defective layer area and the original map, the layered defect is recognized in the whiteboard attention. Since the contrast of the layered defect is not obvious, the precise layered defect tag extraction is another important factor affecting network performance. Based on the fuzzy-c-mean clustering algorithm and expert acceptance principle, obtaining the precise layered defect label, the practicality of this method is further enhanced. The effectiveness of the method for detecting the chip layering defects is verified by testing the chip image provided by Huawei.
A Prediction Model of Alloy Yield in RH Furnace Based On SSA-ELM
Authors: Guo Jiaqing , Liu Huikang , Chai Lin 
Published: April 2022

Abstract
Aiming at the problem that it is difficult to predict the alloy yield in the RH furnace refining process, an alloy yield prediction model based on a sparrow search algorithm (SSA) optimized ELM neural network is proposed. Firstly, because the dimensions of input parameters are different, 11 input features are reduced by factor analysis (FA), and 5 input features are obtained. Then, through the sparrow search algorithm with fast convergence, high precision, and good stability, the input weight value and threshold of the ELM neural network are optimized, the SSA-ELM alloy yield prediction model is established, the alloy yield is predicted, and the off-line operation of the model is realized, which provides a theoretical basis for the prediction of alloy yield in the actual production process. Finally, by comparing the simulation results with the actual production data, it is found that the prediction results of the target element alloy yield predicted by the SSA-ELM are within the error range, and the prediction accuracy is higher than that of the ELM prediction model, which verifies the feasibility and effectiveness of the model.
Study on the 3D point cloud semantic segmentation method of fusion semantic edge detection
Authors: Chen Ling , Xu Gang , Fu Nana , Hu Zhifeng , Zheng Shuzhan , Li Xiang 
Published: April 2022

Abstract
With the continuous development of deep learning, semantic segmentation, as the basis of 3D scene understanding, has also been widely used in 3D point clouds. Semantic segmentation based on the point cloud has obvious advantages due to its rich data. Aiming at the problems of unclear segmentation target and unclear edge in point cloud semantic segmentation, a 3D point cloud semantic segmentation algorithm integrating edge detection was proposed. Firstly, complete global semantic features are obtained by the mainstream 3D point cloud semantic segmentation framework. Then, semantic edge detection is used to extract edge semantic features from the point cloud. Finally, the fusion module fuses the semantic features belonging to the same object to make the segmentation target more accurate. In addition, a dual semantic loss function is used to produce semantic segmentation results with better boundaries. Experimental results show that the improved algorithm has better precision than KPConv in S3DIS and ScanNet datasets and better segmentation performance.
An MMC-based cryogenic calorimeter with a massive sodium molybdate crystal absorber for neutrinoless double beta decay searches
Authors: Kim W.T. , Kim S.C. , Sharma B. , Grigorieva V.D. , Jeon J.A. , Kim H.L. , Kim H.J. , Kim S.G. , Kim S.R. , Kim Y.D. , Kim Y.H. , Lee H.J. , Lee M.H. , Lee S.W. , Shlegel V.N. , Seo J.W. , So J.H. 
Published: April 2022

Abstract
Sodium molybdate crystals are excellent scintillating target material that can be used to investigate the neutrinoless double beta decay of 100Mo.
Because this material contains Na nuclei, it could also be used to clarify the contribution of Na in the controversial claim of dark matter observation by the DAMA/LIBRA experiment.
Recent developments have allowed the growth of sodium molybdate crystals of several hundred grams, which makes it possible to build large scale experiments.
Therefore, a cryogenic calorimeter with a crystal of significant mass, which can exploit the excellent energy resolution from thermal signals and particle identification using the scintillation signals,
is highly desired. We have developed a cryogenic calorimeter with a cylindrical sodium molybdate
crystal of 178 g with dimensions of 4 cm (height) × 4 cm (diameter) coupled to a metallic magnetic calorimeter (MMC).
The detector was also equipped with a light detector with a thin Ge absorber to detect scintillation signals.
The detector test measurements showed excellent energy resolution and particle discrimination using dual detection of heat and light signals,
and thus demonstrate that the crystal is a promising tool for searching for rare phenomena events.
Details of the detector development with the massive sodium molybdate crystal are discussed in this paper along with the performance results from test measurements.
Root traits of sugarcane cultivated by monoculture system in three orders of soil
Authors: Kusumawati A , Hanudin E , Purwanto B H , Nurudin M 
Published: April 2022

Abstract
Sugarcane is a valuable crop and has been cultivated in Indonesia in a monoculture system since the Dutch colonial period. Cultivation of sugarcane in monoculture in the long term affects the condition of soil properties. This will affect plant growth, one of which is plant roots. This study aim was to determine the effect of long-term sugarcane monoculture on sugarcane root conditions in three different soil orders. The research was conducted using two factors oversite design, soil order and duration of monoculture system. The observed soil parameters included soil physical and chemical properties such as bulk density, percentage of sand, silt, clay, porosity, pH H2O, organic matter, cation exchange capacity, NH4
+, NO3
-, Available P, Na, K, Ca, Mg, B and Zn. Root parameters observed were root fresh weight, the weight of root dry, root volume, the length of root, root cation exchange capacity and root surface area. The results showed that root volume, root length, root cation exchange capacity and root surface area were significantly impacted by the interaction of the soil order and the duration of monoculture system.
Effect of biochar and rice irrigartion methods on methane gas emissions
Authors: Saputra Aditya Arief , Utami Sri Nuryani Hidayah , Purwanto Benito Heru 
Published: April 2022

Abstract
Environmental problems related to agricultural production, especially rice, namely rice cultivation is the main cause of increasing global warming. The use of biochar types and types of irrigation can affect the rate of methane gas emission. The purpose of this research was to determine the effect of the treatment of addition of several types of biochar with a combination of irrigation treatment on the rate of methane gas emissions and investigate the rate of methane gas emissions at the time of collection in each phase of plant growth. The design used was a factorial Completely Randomized Block Design with the first factor was type of biochar: namely bamboo biochar by pyrolysis method, bamboo biochar by open firing method and coconut shellbiochar by open firing method, and the second factor was continuous flooding irrigation, macak-macak (0-0.5 cm water level) condition and intermittent with three replications. Soil sampling was carried out during the maximum tillering phase, the flower primordia phase and the grain filling phase. While gas sampling was carried outfive repetitions at intervals of 3 minutes. The results showed that the treatment of coconut shell biochar by open firing method combination with continuous flooding irrigation had the highest flux value of 11.44 × 10-4 (mg/m2/min) at the time of taking 60 days after planting (dap). The number of tillers can affect the CH4 flux value. The highest determination value is at the number of tillers 60 dap followed by 70 dap that is 57.05% and 28.77%, which means that the number of tillers has a significant effect on the CH4 flux value.
Effect of coffee husk compost to increase peanut yield in inceptisol
Authors: Junedi H , Suryanto S , Syukria H 
Published: April 2022

Abstract
The use of compost as an ameliorant has been shown to improve soil quality and increase crop yields. One of the organic materials that can be used as compost is coffee husk. Besides being able to increase the quality and yield of the plant, the rest of the coffee husk will also pollute the environment if it is not utilized. The aims of this study were to evaluate the effect of coffee husk compost application on Inceptisol and peanut yield. The field experiment was done on an Inceptisol, from March to December 2020. The experiment used a randomized block design with 6 treatments of coffee husk compost with various doses and 4 replications, namely without compost, 10 Mgha-1, 15 Mgha-1, 20 Mgha-1, 25 Mgha-1, and 30 Mgha-1. The results showed that the application of 20 Mgha-1 coffee husk compost was the best treatment in optimizing decreasing soil compaction, bulk density, and increasing porosity, while 15 Mgha-1coffee husk compost was the best treatment in increasing peanut yield.
Mathematical Model for Rice (Oryza Sativa) Growth Analysis Under Green Fertilizer of Biogas Slurry
Authors: Hidayat F.U. , Ngadisih  , Karyadi J.N.W. , Tirtalistyani R. , Ismail M.H. , Mawandha H.G 
Published: April 2022

AbstractThe purposes of this study were to observe and measure the growth of rice plants against the application of biogas slurry fertilizer, to calculate the growth rate of rice plants with an appropriate mathematical model, and to recommend a dose of biogas slurry fertilizer for rice cultivation. This study was conducted in an experimental field using plant pots with 5 treatments of applying biogas slurry, namely without treatment/control (K), 60 Mg/ha of liquid slurry (C1), 30 Mg/ha of liquid slurry (C2), 60 Mg/ha of solid slurry (P1), and 30 Mg/ha of solid slurry (P2). Each treatment was repeated 2 times with each plant pot filled with 3 rice plants. The pots were set in randomized complete block design. The growth parameters observed were the number of leaves, plant height, number of tillers, crown weight, and plant root weight. Observations were made for 120 days. The analysis used in this study used one-way ANOVA analysis, linear regression and logistic equation for plant growth rates. The results showed that the P1 treatment had the best growth response. The coefficient value of plant growth rate (μ) on the number of leaves treatment parameters K, C1, C2, P1, and P2 were -0.044; -0.047; -0.0567; -0.0613; and -0.0547, respectively. Then, the plant height parameters were -0.0448; -0.0474; -0.0448; -0.0421; and -0.0458, respectively. The number of tillers in a row were -0.0408; -0.0405; -0.0432; -0.0447; and -0.0448, respectively. The ANOVA test showed that the application of biogas slurry fertilizer was significant (sign < 0.05) on plant growth rate, while it had no significant effect on wet and dry weight of shoots and plant roots (sign > 0.05). Logistic equation was excellent to describe rice growth under biogas slurry fertilizer. The dose of slurry biogas fertilizer that provides the most contact for plants was 60 Mg/ha.
The effect of competency and orientation toward agripreneurship performance on semi-organic shallot farmers: A case study of Selopamioro Village, Imogiri, Bantul District, Indonesia
Authors: Waqingah S , Irham  , Mulyo J H 
Published: April 2022

Abstract
The growth of semi-organic shallot farming in Bantul Regency is increasing due to the support of various development programs that are expected to optimize the ability of natural resources as well as building farmer competencies and orientations in order to create independent farmers as agripreneurs. The objectives of the research is to examine the effect of competency and orientation toward agripreneurship performance on semi-organic shallot farmers. This research was conducted purposively in Selopamioro Village as one of the centers for semi-organic shallot development in Bantul Regency. Data were collected through interviews, observations and literature review then analyzed using PLS-SEM. The research showed that the farmers count more on technical and managerial skills than on entrepreneurial orientation. It is found that competency has a positive influence toward agripreneurship performance, which means that the higher the level of competency, the better the agripreneurship performance will be.