bluuebunny/crossref_metadata_2025 huggingface dataset:
Path planning of manipulator based on improved RRT algorithm
Authors: Wang YuYu , Tian JunCheng , Liu Zhe , Liu HaoBo , Liu JiaZheng , Xie Lin
Published: April 2022

Aiming at the problem that the 6-DOF manipulator can quickly avoid obstacles in high-dimensional space, the method of path planning based on the improved rapid expansion random tree (RRT) algorithm is proposed. The improved RRT algorithm combines the advantages of the target bias strategy and proposes a method for setting the intermediate bias point. In random sampling, the sampling point is biased to the intermediate bias point,with a certain probability to reduce the blindness of sampling, and use at the same time the path shortening method reduces path nodes and turning points, and optimizes the motion path of the robotic arm. By comparing the simulation results with the RRT and P-RRT algorithms, the effectiveness of the improved RRT algorithm in the calculation time and the number of path nodes is proved.
State Estimation of Wireless Sensor Networks Under False Data Injection
Authors: Jin Qingchen , Chen Xin , Zhang Pengfei , Yuan Jing , Li Shang
Published: April 2022

This paper studies the problem of false external data injection information transmitted through wireless sensor networks. Firstly, this paper models the nonlinear system and improves the unscented Kalman filter to solve the problem of false data injection in the transmission process replaces the abnormal innovation data by setting the innovation confidence interval in advance and deduces the error covariance of this method. Then the stability of the designed algorithm is verified. Finally, the effectiveness of the filtering algorithm designed in this paper is illustrated by tracking the state of the system and the mean square error of the simulation system.
A UAV Coverage Path Planning Algorithm Based on Double Deep Q-Network
Authors: Li Shang , Chen Xin , Zhang Min , Jin Qingchen , Guo Yudi , Xing Shunxiang
Published: April 2022

The UAV path planning method has practical value in the military field and automated production. Based on deep reinforcement learning theory and the characteristics of coverage path planning, this paper designs and implements a set of deep reinforcement learning frameworks suitable for UAV coverage path planning and trains it in the abstract environment model built. The simulation experiment results show that the designed UAV coverage path planning frameworks can consider obstacles, no-fly zones and length constraints, plan a reasonable path to complete the coverage task, and have a certain generalization ability.
Research on mmWave IoV Beam Tracking Based on EKF
Authors: Sun Yu , Feng Chenwei , Lin Weiming , Zhang Lin , Zeng Huangbin , Li Zhuo
Published: April 2022

The rapid development of 5G communication provides a huge opportunity for the research on the Internet of vehicles. With the increasing shortage of low-frequency spectrum resources and the demand for high transmission rates for the architecture of the internet of vehicles, the highly qualified millimeter wave has been combined into the research on the internet of vehicles. However, due to the various characteristics of millimeter wave and the limitation of the complex communication environment of the internet of vehicles, how to achieve stable beam tracking is a big problem that needs to be solved urgently. This paper mainly introduces an extended Kalman filter algorithm with updated threshold to solve the problem of continuous stable beam tracking of millimeter waveInternet of vehicles. Simulation shows that this method has lower tracking error than the original algorithm, and better overcomes the problem of continuous accumulation of tracking error of the original algorithm.
Path planning of mobile robots based on improved RRT algorithm
Authors: Liu Benxue , Liu Chong
Published: April 2022

Aiming at the shortcomings of RRT (Rapidly-exploring Research Tree) algorithm such as long time cost and low utilization of sampling points, an improved RRT algorithm is proposed. By adopting the sampling strategy based on dynamic probability, the robot is prevented from falling into a local minimum during the sampling process. At the same time, the adopt of variable step strategy as the random tree expands reduces the number of sampling points. Finally, the initial path is optimized to make it more suitable for the robot to walk. The improved RRT algorithm is compared with the RRT algorithm and the Goal-bias RRT algorithm in both simulations on MATLB and experiments on robot based on ROS (Robot Operating System). And the results show that improved RRT algorithm increases the speed of path planning, reduces the length of path, and the planned path is smoother and more suitable for the real robot to move.
Dual-arm Robot Fast Pick-and-Place on a Moving Conveyor
Authors: Sheng Jiangcong , He Dingxin , Gu Qiang , Gong Dongjun
Published: April 2022

The pick-and-place (PNP) system of multi-robots on moving con- veyors have a wide range of industrial applications. In practice, the most used is first in first out (FIFO) algorithm. However, the efficiency of FIFO is low when the number of objects to be picked is large. To improve the PNP efficiency, we first build a kinematic model of the dual-arm robot and establish a PNP time lookup table based on the inverse kinematic algorithm. Then, we develop a PNP shortest time first out (STFO) algorithm to calculate the high-efficiency picking sequence. Dynamic experiments show that compared with FIFO, we improve the dual-arm robot PNP system efficiency by 20%.
A Method of Robot Grasping Based on Reinforcement Learning
Authors: Lu Liangliang , Zhang Ming , He Dingxin , Gu Qiang , Gong Dongjun , Fu Lei
Published: April 2022

In this paper, we propose a method for robot grasping based on reinforcement learning. A six-degree-of-freedom robot is used to execute the grasping action, and an RGB-D camera is used as a sensing module to collect the information. Different from previous model-based methods, our whole system is data-driven. In order to better grasp a bunch of randomly placed objects, the pushing action is added to spare more space for grasping. The whole process is described as a Markov decision process (MDP). The training is carried out in simulation. Experimental results show that our method can complete the grasping work efficiently, especially in those complicated and difficult scenarios. At the same time, the model can be directly applied in actual grasping tasks which greatly reduce the risk of training in the physical world.
Attitude Control Design and Simulation Analysis of Quadrotor Based on Active Disturbance Rejection Control
Authors: Cheng ZiLong , Liu Jia , Zhang MingFeng , Zhao Nan , Wang MengYuan , Zhang YunXi
Published: April 2022

In this paper, the attitude control problem based on active disturbance rejection control (ADRC) is studied for nonlinear underactuated quadrotor aircraft with parametric uncertainties and modeling disturbances. The simplified dynamics model of the quadrotor is established by using Newton-Euler equation, and the ADRC is applied to control the attitude control of the quadrotor. Finally, through numerical simulations, the performance of the ADRC and traditional PID algorithm are compared, the results show that the attitude control algorithm based on ADRC has better performance, and has excellent ability to resist disturbance.
Research on Swing Prediction of Overhead Line Robot Inspection Arm
Authors: Guo Xinliang , Liu Ronghai , Cai Xiaobin , Shen Feng
Published: April 2022

Overhead lines and various fittings on the lines are prone to damage when exposed to the outdoor environment for a long time. Therefore, overhead line inspection work is required. The use of overhead line robots instead of manual inspections can reduce the labor intensity of personnel and protect their personal safety. The current overhead line robots have the problem of low intelligence and less consideration of the robot swinging with the wire. In this regard, the paper focuses on the swing prediction technology for the large-angle swing of the robot platform, and proposes a method based on real-time variance statistics. The large-angle swing prediction model. Finally, a comparative experiment was conducted with the combined model of empirical mode decomposition and support vector regression based on time series forecasting, and real experimental data were used to verify the rationality and effectiveness of the proposed algorithm and model.
An Improved Genetic Algorithm for Rapid UAV Path Planning
Authors: Liu Jinrong
Published: April 2022

UAV technologies have advanced rapidly and are widely used in military and civilian fields. For instance, the UAV swarms have been widely applied to oil and gas exploration, geometric mapping, and cargo transport. However, the UAV swarm system requires a more accurate plan before performing any mission. Path planning is one of the essential parts of mission planning because of the higher requirement of robustness and real-time communication. UAV path planning could generate the optimal path starting from the current position to the target in an environment with an obstacle. While the standard genetic algorithm has lacked efficiency in the iteration process and poor stability, a new genetic operator is proposed for the genetic algorithm and applied to the path planning simulation of UAV swarms in this study. A three-dimensional mapping and fitness function are already constructed for the simulation. The simulation result shows the algorithm with improved selection and mutation operator can efficiently and stably converge to the optimal solution.
SLIDING MODE CONTROL OF UNMANNED AERIAL VEHICLE BASED FINITE-TIME DISTURBANCE OBSERVER
Authors: Cui Lichao , Jiang Yulian , Zhang Xudong
Published: April 2022

Because of the advantages of high maneuverability, vertical takeoff and landing, free hovering, and easy portability, quadrotor UAVs have become one of the research hotspots in recent years. Aiming at the finite-time trajectory tracking control problem for quadrotor under unknown environmental disturbance and modeling uncertainty, a fast terminal sliding mode control method using a finite-time disturbance observer is proposed. First, the modeling uncertainty and unknown external disturbance of the quadrotor were combined into lumped disturbance, and a finite-time disturbance observer was designed to estimate the lumped disturbance online. Secondly, a fast terminal sliding mode controller was constructed for the realization of a finite-time tracking control of the quadrotor, and the estimated value of the limited-time disturbance observer was compensated to the controller. Thereby, the influence of compound disturbance was eliminated, the quadrotor can achieve a finite-time trajectory, and the disturbance suppression capability of tracking control was improved. Finally, we conducted simulation experiments and confirmed that the control strategy proposed in this paper is effective.
Research on Guidance and Control Method of Hypersonic UAV Capture Segment Based on Overload
Authors: Zhang Dong , Li Xiujuan , Han Xiao
Published: April 2022

With the implementation of China’s high supersonic aircraft science and technology engineering and the key basic scientific problems of near space vehicles major research programs.In this context, China is also vigorously developing high-speed high-mobility, long range and recyclable aircraft. In view of the tracking problem of a hypersonic vehicle when it reaches the capture segment, a mathod based on overload guidance law is proposed. The relationship between force and head-on angle can be deduced from the relationship between overload and head-on angle, thus transforming the overload control problem into the head-on control problem. Inversion controller design method is adopted for its parameter uncertainty rigid body model, and the derivative of virtual control amount is obtained by dynamic surface method to avoid the problem of differential expansion. Based on the secound-order tracking-differential, a nonlinear interference observer is designed to estimate and compensate for model uncertainties. The simulation results show that the controller can achieve stable tracking of the angle and high reference instructions, and has a strong robustness to the uncertainty effect of the model.
Design of Multi-liquid Mixing Control System Based on S7-300
Authors: Ji Jun , Huang Dun-Hua , Xing Fei-Fei
Published: April 2022

This article introduces the application scenarios of a variety of liquid mixing control devices, studies the analog and function knowledge points involved in the control system, analyzes the control requirements, and designs the program.
A compound convolutional neural network used to process small perturbation
Authors: Ni W.H , Xu Shu
Published: April 2022

Input samples formed by deliberately adding subtle interference to the data set may cause the model to give false outputs with high confidence. To solve this problem, this paper proposes a deep learning method. Different interference sources can be considered to be independent of each other. Therefore, different networks are trained based on densenet for different interference sources. Then, the data set added to the combined interference item is sent to each neural network, and the output of each network is used as the input of the neural network in the next stage. The next stage of the neural network is used for multi-objective classification based on the output of the neural network. The output of the neural network is disturbed by adding different features to the input data. Because the depth network of each type of interference is independent, when introducing a new interference term, only the depth network and full connection layer of the new interference term need to be trained, rather than the whole network, which greatly simplifies the training process when introducing a new interference source.
Fault diagnosis of Fuel Pump Based on Wavelet Denoising and Deep Learning
Authors: Guo Yudi , Chen Xin , Wang Weizhen
Published: April 2022

Aiming at the problem that the signal collected by the sensor of aircraft fuel system contains much noise, which masks the effective fault characteristics, a fault diagnosis method of aircraft fuel pump based on wavelet threshold denoising and convolutional neural network is proposed. Wavelet threshold denoising is added to the traditional data preprocessing to eliminate the noise contained in the data; the convolution neural network is used to build a fault detection model to identify and locate the fault of the fuel pump. The experimental results show that this method can accurately and effectively identify and locate the fuel pump fault based on the noisy signal, and the recognition accuracy is more than 85%, which proves the effectiveness of this method for aircraft fuel pump detection.
A new type of microgrid power quality improvement strategy based on harmonic suppression
Authors: Wang Mian , Liu TianYu
Published: April 2022

Current harmonics and distorted voltage are two important factors that affect the power quality of microgrids. Combining with the operating characteristics of microgrids, this article uses a new strategy to control grid-connected inverters and UPQC to improve grid quality. Pointed out its advantages relative to traditional active filters, and in view of the current harmonic detection problems, In this paper, the inverter adopts an improved ip-iq harmonic detection method to improve the accuracy of harmonic current detection and has strong interference. The UPQC structure uses SVPWM and PWM control methods as its new control strategies on the series side and parallel side to compensate for distortion voltage and harmonic current. A detailed model is built in MATLAB/Simulink to verify the feasibility of this method.
A Fast Path Planing Solution For Multiple Obstacle Spaces
Authors: Chen Xiaolei , Shen Haoyang , Yuan Junling , Han Lu
Published: April 2022

Among the many path planning algorithms, the RRT* algorithm and the Informed RRT* algorithm are the most popular. Both of them can complete the single path planning effectively. However, because the planning effect of these algorithms is limited by the number of iterations, the optimal solution cannot be planned in a limited time. Therefore, a new algorithm-Self-Optimizing Growing Tree (SOGT) is proposed in this paper. On the premise of ensuring complete probability, heuristic bias sampling is used to improve the efficiency of the algorithm to explore free space. Then, the initial path is optimized by the maximum distance and the inflection point optimization using prior information to obtain an optimal path. In the simulation experiments, we give the comparison results of numerical simulations of SOGT algorithm, RRT* algorithm and Informed RRT* algorithm. The experiment proves that the SOGT algorithm can get an optimal path in less time, which fully reflects the characteristics of the algorithm with good real-time performance and strong adaptability.
Neural Network based Identification and Self-tuning PID Control for Spacecraft Thermal Vacuum Tests
Authors: Wu Dongliang , Zhu Xi , Wen Jing , Zhu Lin , Feng Yao
Published: April 2022

This paper deals with the high-performance temperature control issues of spacecraft thermal vacuum tests. More precisely, the novel model identification and self-tuning PID control method is developed based on neural network. The corresponding controller design details are provided and the experiment results are given for verifying considerable advantages with our proposed control strategy in the end.
A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation
Authors: Jia Jiangbo , Chen Xin , Zhang Min , Li Zhen
Published: April 2022

This paper investigates a circular formation control strategy for multiple unmanned aerial vehicles (multi-UAV) formation for ground moving target tracking. Each UAV obtains the observation value of target information through sensors and exchanges data with its neighbors. Since the formation cannot get the parameter of the target directly, we proposed a distributed error observer. A new guidance law based on the error value obtained has been designed, and it was proved through the interconnection system that it was consistent with Lyapunov stability. Finally, a simulation example is used to illustrate the effectiveness of the proposed guidance law.
A two-stage modular blind denoising algorithm based on real scene
Authors: Ding Yuehao , Wu Hao , Yuan Guowu
Published: April 2022

The use of deep learning methods has developed rapidly in image denoising. Now deep learning methods have a good improvement in denoising effects compared to traditional methods, but there are also some problems. On the one hand, these denoisers are more used to remove the noise of a specific distribution, while the image noise distribution in real life is not fixed, so it is more difficult and more practical to denoise the real image. On the other hand, more and more complex network structures and deeper and deeper network models seem to have become a necessary condition for better denoising effects, but its improvement is not linear, and deeper networks are likely to be removed. The improvement in noise effect is minimal. Because of this, we designed a relatively simple network structure to remove real image noise, which includes the noise level estimation stage using the channel attention mechanism, and the non-blind noise reduction stage using the micro-branch structure we designed. Experiments show that our method has good visual perception quality compared with other methods on commonly used image denoising data sets.
Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning
Authors: Chen Jing , Chen Xin , Fu Zaifei
Published: April 2022

Seagull Optimization Algorithm (SOA) is an emerging intelligent optimization algorithm proposed in recent years. This paper proposes an improvement in the SOA based on Levy flight(LSOA), which aims to solve the problems of decreasing exploration ability and easing to fall into local extreme values in the late stage of SOA. LSOA could increase the diversity of the seagull population, improve the exploration ability of SOA. Then selecting the classic 23 benchmark functions for simulation experiments and comparing with the other five intelligent algorithms in recent years. The result shows that LSOA has improved stability, convergence speed, and search accuracy, effectively avoiding local optimum conditions, with broad research prospects. Finally, LSOA is applied to the path planning problem, proving the algorithm’s feasibility and having a good optimization effect.
A Multi-feature Combination Method for Tracking of Marine Ships
Authors: Yang Lichun , Yang Dan , Wang Fan
Published: April 2022

Maritime images and videos provide a variety of kinematic traffic information, including traffic volume, ship speeds and headings, which significantly benefits remote maritime traffic management and maritime safety improvement. To track multiple moving objects robustly from the complex original maritime image sequence, we propose an ensemble ship tracking framework with multiple feature extraction and Dempster–Shafer theory of evidence. Spatial features such as statistical features of gray values and geometric features in the image sequence rather than the motion characteristics are extracted, and the D-S theory is applied for combination of feature evidence to perform the correct association of multiple objects. We demonstrate the performance of the proposed ship tracking algorithm on a typical marine traffic scene through the marine surveillance video. Experimental results show that the proposed method can achieve continuous tracking of multiple marine moving objects with strong stability, and have the potential to improve marine transportation and fishery safety.
Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*
Authors: Du Linlin , Tang Hao , Li Pengfei , Ma Tao , Ge Shuangquan , Cao Kang
Published: April 2022

Navigating in an unknown area safely is counted as the underlying work which can support swarm agents for more complex tasks. When available information of search regions are lacking, agents make real-time action decisions according to surrounding environments they have perceived. For swarm agent system, connectivity maintenance and collision avoidance are both essential. Based on optimal Reciprocal Collision Avoidance (ORCA) algorithm, we proposed a method that agents can provide assistances to surrounding agents by spreading the status information of themselves, which is the neighbor reward method (NRM). This kind of status information contains ambient information and perceptions of the task which are transferred to reward data for convenient and uniform distributions. In other words, individuals utilize inter-neighbor interactions to achieve the same high-level goal, as well as result in an intelligent independent swarm agents system.

This method solves the velocity selection problem of ORCA and optimizes the obstacle avoidance of the original NRM. The algorithm has been integrated in ROS framework and simulated on GAZEBO. In the tested scenario, our method is efficient for swarm agents collision avoidance in decentralized way.
Asynchronous Federated Learning for Elephant Flow Detection in Software Defined Networking Systems
Authors: Ma Xiaohang , Liao Ling Xia , Li Zhi , Chao Han-Chieh
Published: April 2022

This paper introduces an Asynchronous Federated Learning (AFL) approach to train an elephant flow model over Software Defined Networking (SDN) systems with distributed controllers. The AFL addresses the issues of data privacy and communication overhead in collecting network statistics over large-scaled SDN systems. It allows each local controller to train a local model based on its local statistics using Decision Tree and upload the local model to the root in an asynchronous manner, so that the root controller can aggregate each local model into a global model once a local model is received to improve its time efficiency. The AFL proposes to weight the performance of each local model to form the global model. The evaluation based on 5 real packet traces demonstrates the accuracy of the AFL is better than any local models and two classical federated learning approaches.
Loop Closure Detection for SLAM Based on Siamese Convolutional Neural Network
Authors: Li Xiang , Xu Gang , Zhang Xingyu , Yu Ze , Zhu Zhimin
Published: April 2022

Loop closure detection is an indispensable part of the SLAM system, which can reduce the cumulative error of mobile robots in positioning and mapping. This paper proposes a supervised learning method, which combines the Siamese network and the VGG16 network in deep learning to handle loop closure detection. The Siamese network method and the traditional method are contrasted in the New College and City Centre data sets to prove the method’s usefulness in this research. The experiment’s findings reveal that the method of this paper ensures precision and recall rate while reducing the time necessary to assess similarities compared to the traditional method. It has a better chance of achieving real-time SLAM.
Channel Attention Module for Efficient Action Recognition
Authors: Cheng Xianpeng , Lu Fei , Han Xiangzu , Yuan Ying , Tian Guohui , Wu Hao
Published: April 2022

For action recognition, 3D CNNs can achieve satisfactory performance, but they are parameter-rich and computationally intensive. Although conventional 2D CNNs have a small computational burden, they do not capture the channel relationships well. To mitigate this issue, we propose a plug-and-play channel attention module that is simple yet effective. Our CA module can perform adaptive refinement of the feature for the given input feature map by capturing channel-wise dependencies. Since the CA module is generic and lightweight, it can be easily integrated into regular convolutional neural networks. We embedded the CA module into TSM and evaluated it by conducting extensive experiments on the commonly used action recognition dataset: Kinetics-400 and gesture recognition dataset: Jester. For Kinetics-400, our CA module was able to improve the top-1 accuracy by 1.24%, 1.46%, 1.43% and 1.53% when using one crop, three crops, five crops and ten crops, respectively. Our experimental results showed that the CA module brings consistent performance improvements, proving the effectiveness of the CA module.
Droplet Image Reconstruction Based on Generative Adversarial Network
Authors: Zhang Jinsong , Chen Haiyan , Wang Zhiliang
Published: April 2022

In the digital microfluidic experiments, the improper adjustments of the camera focus and background illumination lead to the phenomena of low illumination and blurred edges in the droplet image, which seriously interferes with information acquisition. Removing these blurred factors is an essential pretreatment step before information extraction. In this paper, a generative adversarial network model combining multi-scale convolution and attention mechanism is proposed to reconstruct the droplet image. The feature reconstruction module in generator can reconstruct the image feature maps from multiple scales. The fusion module is used to fuse the multi-scale feature maps into a reconstructed sharp image. The new model was trained on the data set which was made by the Style Transfer. Experimental results show that the proposed model can significantly improve the visual quality of images, effectively reduce the blur and improve the background illumination.
An Elderly Indoor Behavior Recognition Method Based on Improved SlowFast Network*
Authors: He Junping , Xiang Min , Zhao Xiaoxiang
Published: April 2022

Due to the limited resources of home-based elderly care monitoring equipment, how to accurately identify the elderly behavior is a hot issue concerned by many equipment manufacturers. A behavior recognition method suitable for embedded equipment is proposed. This method first introduces the TPN module in the SlowFast network to capture the visual action tempo of the elderly and uses Faster-CNN to detect the elderly and objects in the video frames. Then in the AIA module, the accuracy of the elderly indoor behavior recognition is improved by strengthening the correlation between the elderly and the objects. Experiments show that the improved network can run stably at 18FPS on Jetson Xavier NX. The indoor behavior recognition accuracy for the elderly reaches 90.61%, which is 8.25% higher than the original SlowFast network.
Multi-label Image Classification Model via Label Correlation Matrix
Authors: Huang Tianxi , Wu Dongdong , Duan Guiduo , Huang Hao
Published: April 2022

Multi-label classification is one of the most essential tasks of computer vision. In the multi-label image classification model, using the correlation between labels is a powerful method for improving the classification effectiveness of the model. However, common methods ignore the interrelationship between the label pairs. On the other hand, introducing a spatial attention mechanism into the model could also improve the classification effectiveness of the model. However, most methods that use the attention mechanism module do not use the correlation information between labels. To solve these issues, we propose a novel multi-label image classification model using the label correlation in the paper. Our model generates label word vectors based on the BERT model that can describe the potential relationship between labels. And then we combine these vectors with static statistics information on labels to construct a new label correlation matrix. Moreover, we introduce label semantic information into the spatial attention mechanism. With the semantic information, the generated spatial attention map could focus on the image feature regions with label correlation, and complete the accurate classification of the model. On the Microsoft COCO data set, this model achieves the best score of 84.3% on mAP, which shows the effectiveness of our model.
Design of an Internet of Things — based multi-channel temperature monitoring system
Authors: Clerbaux B. , Gómez de Gracia D. , Petitjean P. , Yang Y.
Published: April 2022

In the scope of the Jiangmen Underground Neutrino Observatory (JUNO) project, 6 back-end card (BEC) mezzanines connected to one BEC base board are in charge of compensating the attenuated incoming data from 48 front-end channels over 48 100-meter-long Ethernet cables. Each of the mezzanines has 16 equalizers that may be subject to overheating. It is important therefore to monitor their temperature in real time. However, collecting data from a relatively large (1080) number of mezzanines is not a trivial task. In this work we propose a solution based on Wi-Fi mesh. Both the technical details and the first test results performed are reported.
Study of the spatiotemporal structure of extensive air showers at high energies
Authors: Kalikulov O.A. , Saduyev N.O. , Shaulov S.B. , Zhukov V.V. , Beznosko D. , Mukhamejanov Y.S. , Yerezhep N.O. , Baktoraz A.Y. , Shinbulatov S.K. , Utey Sh. , Zhumabayev A.I. , Sedov A.N. , Oskomov V.V.
Published: April 2022

In a series of works carried out at the Tien-Shan High Altitude Science Station (TSHASS), a penetrating component of extensive air showers (EAS) caused by cosmic rays (CR) with energy of the primary particle above 1 PeV was discovered. The local increase in the energy of secondary hadrons in the EAS cores in the region of the CR spectrum slope change around energy of 3 PeV, the so-called “knee”. The problem of knee formation currently is one of the currently still open mysteries in astrophysics. The studies carried out at the TSHASS show that more information on the knee region can be obtained by studying the characteristics of the EAS cores. At present, a large installation with an area of several hundred square meters is being constructed at the station to study the EAS cores in the “knee” region. The installation is located at an altitude of 3330 meters above sea level near the city of Almaty, Kazakhstan.
Save our soil from heavy metals (Pb and Cd) accumulation for rice growth
Authors: Budianta D , Napoleon A , Merismon  , Habi M L
Published: April 2022

Rice intensification has been very successful in Indonesia and was even appreciated by FAO in 1985. However, despite the success, paddy soils are frequently polluted with Pb and Cd which are mostly derived from phosphate (P) fertilizers. Considering that the soils have been used for the long-term cultivation, Pb and Cd contents both in the soil and rice have exceeded the tolerance level in food as defined by WHO namely 2 and 0.24 mg kg-1, respectively. The highest Pb and Cd were found in soils cultivated for 80 years in Musi Rawas, Indonesia with values of 20.56 mg kg-1and 0.72 mg kg-1for soil and 3.11 mg kg-1and 0.29 mg kg-1for rice, while the lowest was found at 20 years with values of 17.82 mg kg-1and 0.26 mg kg-1. Furthermore, the highest rice Pb and Cd were obtained at 80 years of cultivation with values of 3.11 and 0.29 mg kg-1, respectively, while the lowest was found at 20 years with values of 2.35 mg kg-1Pb and 0.15 mg kg-1Cd. Therefore, these metals need to be managed immediately. One of the methods to detoxify heavy metals is by application of organic material. Through a chelating process, heavy metals are immobilized and not absorbed by rice. Based on the results, rice straw biochar and compost at a rate of 10 tons ha-1separately reduced Pb and Cd in soil from 13.60 to 3.00 mg kg-1and 13.60 to 5.98 mg kg-1for Pb and 0.62 down to 0.29 mg kg-1and 0.64 down to 0.42 mg kg-1for Cd, respectively.
Effect of combination of inorganic fertilizer and biochar-coastal sediment on nutrient availability and growth of corn plants in alluvial soil
Authors: Sulakhudin  , Manurung R , Abdurrahman T , Karpriana A P
Published: April 2022

Fertilizer is one of the inputs in plant cultivation that requires high costs. However, unfavourable soil conditions so that most of the nutrients contained in fertilizers cannot be absorbed by plant roots. The purpose of this study was to increase the availability of nutrients and plant growth by applying a mixture of biochar – coastal sediment with inorganic fertilizers on alluvial soil. The study was a single factor experiment arranged in a completely randomized design with five replications. Treatment consisted of A: without giving fertilizer, treatment B: giving inorganic fertilizer 100% of the recommended dosage, treatment C: inorganic fertilizer mixed with biochar and coastal sediment 75% + 25% fertilizer recommended dosage, treatment D: inorganic fertilizer mixed with biochar and coastal sediment 50% + 50% fertilizer recommended dosage and treatment, E: inorganic fertilizer mixed with biochar and coastal sediment 25% + 75% fertilizer recommended dosage and treatment F: inorganic fertilizer mixed with biochar and coastal sediment 100% recommended dosage. The application of inorganic fertilizer mixed with biochar and coastal sediment 100% recommended dosage was able to increase the availability of nutrients P, K, Mg and growth of corn plants in Alluvial.
Can application of organic matter, cover crops and tree planting improve infiltration rate of soil covered by pyroclastic materials?
Authors: Agustina C. , Utami S.R. , Kusuma Z.
Published: April 2022

Pyroclastic materials from volcanic eruption is easily compacted and may develop surface crust, which potentially decreases water infiltration. We conducted research in Ngantang, regularly affected by Mt. Kelud eruption, as an attempt to improve water infiltration using organic matter and cover crop. The organic matter (20 Mg.ha-1) used was sweet potato leaves (Bsp) and Tithonia diversifolia leaves (Btd). Cover crops (Arachis pintoi (Tap) and Tithonia diversifolia (Ttd)) and tree (Paraserianthes falcataria (P1)) were planted. Ring infiltrometer was used to measure infiltration rate on initial soil, on soils covered by pyroclastics after 3 months organic matter and cover crops application, and after one year tree planting. The result showed that infiltration rate of initial soil was significantly decreased with time, when covered by eruption materials. After 3 months application of organic matter and cover crops, infiltration rate was significantly higher than untreated soil covered by volcanic materials. Planting tree increased infiltration rate with the highest infiltration rate occurred in combination with organic matter Tithonia diversifolia leaves and Tithonia diversifolia cover crops. Infiltration rate increased with increasing total pores, but decreased with increasing of meso-pores, especially 12 months after application organic matter, together with planting cover crops and tree.
Characteristics of peat functional group in Padang Island, Indonesia
Authors: Devangsari I M , Nurudin M , Sartohadi J , Karmila Y A
Published: April 2022

Peat is composed of a diverse range of polar and nonpolar groups. Nonpolar groups, particularly those composed of aliphatic structures, tend to associate with one another and are therefore water-repellent (hydrophobic), a property that increases with the length of the chain of bonds. The drying process causes the nonpolar groups to interact with each other and form a long chain of bonds, reducing the ability of peatlands to bind water. As a result, it is vital to understand the chemical characteristics and functional groups associated with hydrophilicity and hydrophobicity. The chemical analysis comprises the following: ash content (500°C combustion method), C-org using the combustion method, N-total by Kjeldahl method, actual pH with soil water ratio of 1:5, and cation exchange capacity with NH4Oac pH 4.0. Additionally, the pelletized peat was examined for its constituent groups using an FTIR spectra and the Flaig et al., method (1975). Soil pH values on Padang Island ranged from 3.61 to 4.35 and the content of bases Ca, Mg, K, and Na in oligotrophic peat is very low, especially in thick peat (0-5 m). C-organic levels on Padang Island peat ranged from 4.42 to 53.32 percent, according to the results of the analysis. Peat with a thickness 0-5 m has functional groups dominated by C-O from ester, eter, fenol, while peat with a thickness 5-7 m, 7-9 m, >9 m is dominated with H OH group bonds, free OH.
The Effect of Food Consumption Management on the Reduction of Food Waste in Indonesia
Authors: Mulyo J.H. , Widada A.W. , Perwitasari H. , Sugiyarto  , Rohmah F.
Published: April 2022

Food waste is one of the important problems faced by households because it is related to the availability of food in the community. In addition, food wastage is suspected to be closely related to the ineffective and inefficient of food consumption management. This study aims to determine food consumption management, food waste and the effect of food consumption management on food waste in four major cities in Indonesia, namely Yogyakarta, Surabaya, Medan and Denpasar. The latent variable of food consumption management is reflected by four indicators, they are food planning, processing and storage, and food waste disposal. Meanwhile, the latent variable of food waste is reflected by three indicators, including opportunities for food wastage, knowledge about food wastage, and skills in managing food. Indicators in each latent variable is measured using a Likert scale. The data collection method is convenient sampling to get 150 households’ sample from the four big cities. Food consumption management and food waste were analysed descriptive qualitatively, while the effect of food consumption management on food waste was analysed using the Structural Equation Model (SEM). The results showed that the food planning indicators on the latent variable of food consumption management were in the very good category, while the other three indicators, namely food processing, food storage and food waste disposal, were in good category. The three indicators on the latent variable of food waste consisting of opportunities for food waste, knowledge about food waste and food management skills are included in good category. Furthermore, the results of the study show that good management of food consumption has a positive and significant effect on the efforts to reduce food waste at the household level in four major cities in Indonesia.
Factors Influenced Farmer’s Willingness to Continue Semi Organic Shallot Farming In Bantul District, Daerah Istimewa Yogyakarta
Authors: Torani D V , Suryantini A , Irham
Published: April 2022

The agricultural sector is a strategic sector and have an important role in the national economy and the survival of the community, especially in its contribution to GDP, providing employment and providing domestic food. For sustainable agriculture to protect the environment, overuse of pesticides has its drawbacks and its misuse can have serious health implications therefore immediately switch to eco-friendly agriculture with reduced chemicals. So it is important to know the factors that make farmers willingness to continue semi organic shallot farming. The purpose of this study was to find out how the level of willingness of farmers to continue semi-organic shallot farming in Bantul Regency, Yogyakarta and the factors that influence it. The study was conducted on 153 shallot farmers the members of Lestari Mulyo and Srunggo Farmer Group in Bantul District, D.I Yogyakarta. Data obtained by surveyechniques and processed using quantitative analysis. The results showed that the sustainability level of shallot farming willingness to continue by the economic, ecological and social aspect are quite sustainable. . To increase the willingness of farmers to continue their farming, it is necessary to optimize the role of farmer groups by involving government participation in farming training, marketing and economic benefits in semi-organic shallot farming.
Impacts of agricultural waste and NPK fertilizers on soil chemical properties, production and phosphorus uptake of sweet corn plants on ultisol soil
Authors: Panjaitan E , Sidauruk L , Manalu C J , Pratiwi S , Saragih M , Sianturi P
Published: April 2022

Ultisol soil is classified as a marginal terrain with a low productivity level. Thus, this research aims to determine the most fitting biochar material and the effects of NPK fertilizer regarding the chemical properties of Ultisol soil that are analyzed on the production and phosphorus uptake of sweet corn plants. The materials used for the biochar consist of agricultural waste. This research utilized factorial randomized block design which was repeated three times. Factor number one consisted of three types of biochar: rice husk, corncob, and coconut shell. Factor number two was made up of NPK fertilizer which consisted of three dosages: without NPK, 2 g pot-1, and 4 g pot-1. This research was conducted on December 2019 up to March 2020. Research shows that there was no interaction between biochar types and NPK fertilizer, whether on the chemical properties of Ultisol soil, growth parameter, and production nor phosphorus uptake of the sweet corn plants. Corncob biochar was better than other types in affecting the chemical properties of Ultisol soil, production, root dry weight and phosphorus uptake by the sweet corn plants. Application of 4 g pot-1 NPK fertilizer was the best for almost all observation parameters.
Satellite Remote Sensing Using Earth Observing System in Environmental Monitoring for Hydropower & Floating Photovoltaic Reservoir (Case Study: Algae Blooming on Cirata Reservoir, West Java-Indonesia)
Authors: Sibuea R T , Pratna R , Hapsari A , Kristi Y W
Published: April 2022

This paper describes the application remote sensing satellite for vegetation in the management of the Cirata Reservoir. This reservoir is a provider of raw water for the Cirata hydropower plant, which is the largest hydropower plant in Indonesia. In the next 2 years, the largest floating solar power plant in Indonesia and Southeast Asia will also be built here. The results of monitoring using Earth Observing System, the area of water hyacinth in the Cirata Reservoir is in the range of 100 to 400 hectares. Cleaning of water hyacinth per month is in the range of 25 to 75 hectares per month. This has not been able to compensate for the water hyacinth area cover that can develop, especially during the rainy season which has an impact on increasing the Water Level. Therefore, this paper also presents several management strategies that have been carried out. In Indonesia in particular, there are many reservoirs that have the potential for algae blooming disturbances and the potential for floating solar power plants in the future. The experiment conducted in Cirata can be an alternative in best practice management.
Systematic Review of the Efficiency of Aquatic Plants in the Wastewater Treatment
Authors: Palomino Seguil Y. , Vilchez Garay L. , Matencios Cortez C. , Cornejo Tueros J. , Camargo Hinostroza S. , Canales Guerra V.
Published: April 2022

Wastewater treatment uses a phytoremediation strategy that sets significant trends according to recent research from the 2019-2021 period, being one of the most efficient strategies regarding the removal of pollutants using artificial wetlands with aquatic plants. Therefore, the objective of the review article is to determine the efficiency of the aquatic plant in the phytoremediation process for the treatment of industrial, domestic, and municipal wastewater through a systematic mapping method that allows us to summarize the theoretical framework avoiding. The exclusions also have a comparative descriptive design for the study variables where it has been found that Eichhornia Crassipes is an aquatic weed that removed NO2- and NO3- up to 93% of industrial wastewater, in Pistia stratiotes it removed Turbidity (98.5%), N total (100%), P total (100%) and COD (79.18%) in household wastewater in 60 days. Finally, Azolla Filiculoides removed SO4
2- (83%), Cl (76%), PO4
3- (84%), NO3- (76%), COD (79%), BOD (63%) and EC (49%) from municipal wastewater in 21 days.
Polyethylene Terephthalate Waste as a Mobilizer for Heavy Metals in Gold Mine Tailings
Authors: Mashifana Tebogo
Published: April 2022

The accumulation of gold mine tailings (GMT) and polyethylene terephthalate plastic waste (PW) continue to impact the ecosystem negatively, and strategies are required to counteract these issues to maintain a sustainable environment. This study investigated the use of PW as a binder to mobilize heavy metals in GMT. Plastic was used as a binder to develop specimens. were developed using plastic as a binder, the geotechnical and geochemical properties of the specimens were studied. The leachability of heavy metals from developed specimens was also studied. GMT and PW ratios of 80:20; 70:30, and 60:40 were investigated. The mineralogy and elemental composition of GMT and developed specimens showed that was quartz and silicon oxide were the predominant constituent in the materials. The specimen with the highest unconfined compressive strength yielded the lowest water absorption. The concentration of heavy metals in the PW-based specimens was lower than in the raw GMT. Waste plastic can be used as a sustainable binder to mobilize contaminants in GMT. The proposed solution can solve and prevent environmental pollution caused by GMT and WP.
Effect of Synthesis Conditions on Mechanical Strength of Aluminium Slag Modified Waste Foundry Sand Geopolymers
Authors: Sithole N.T , Nkosi M.T.M
Published: April 2022

The waste foundry sand was alkaline activated into geopolymers to reduce their environmental pollution. The waste foundry sand was blended with aluminium slag and alkali activated to produce geopolymers. Aluminium slag was used to supplement WFS with alumina because as it has low content of alumina. The main parameters tested were the effect alumina content, concentration of alkali, liquid to solid ratio, and temperature. The optimum synthesis parameters were a geopolymer prepared with 2% aluminium, 3 M NaOH solution, liquid solid ratio of 0.15 and curing temperature and time of 80°C and 5 days respectively. The optimum parameters yielded the highest UCS of 4.7 MPa. The developed aluminium slag modified waste foundry sand geopolymers met the minimum requirements for ASTM C126-99 and ASTM C216-07a.