bluuebunny/crossref_metadata_2025 huggingface dataset:
Particle Swarm Optimization with Multiple Adaptive Subswarms
Authors: Jiang Xunlin , Yue Yingjie , Min Yinxing , Zhang Qingyu , Gui Xinxing
Published: February 2021

Benefiting from its simplicity and efficiency, particle swarm optimization (PSO) algorithm has shown great performance on various problems. However, for different optimization problems or different search areas, it is still difficult to achieve a satisfying trade-off between exploration and exploitation. On the basis of canonical PSO algorithm, a variety of improved algorithms have been proposed, which have different capabilities of exploitation and exploration, and each of them performs effective in some problems. This paper proposes a particle swarm optimization with multiple adaptive sub-swarms (PSOMAS). It uses multiple subswarms strategy, in which each sub-swarm is evolved by different algorithms, and an adaptive strategy is also used to reduce the consumption of computing resources. A comprehensive experimental study is conducted on 30 benchmark functions, to compare with several well-known variants of PSO algorithms. The results show that PSOMAS with RT=100 could obtain a better overall performance than all others. Moreover, PSOMAS could find high-quality solution in different problems by varying the value of RT.
Global Contrastive Person Re-identification
Authors: Pei Shengyu , Fan Xiaoping
Published: February 2021

Solving the problem of pedestrians being occluded by objects is extremely challenging. Using part-level features to describe pedestrian images can provide fine-grained information. However, only paying attention to the local features of body will lack global pedestrian information. And the network consumes time and memory. To solve these problems, we propose a new person re-identification network. The network uses a global contrastivemodule to obtain the features of pedestrians. Through effective use of the pedestrian’s global features, as well as the pedestrian’s personal information and global contrastive information, the pedestrian can be found in the object occlusion to provide a reliable feature embedding. Our model is tested on Market1501, Duke MTMC-reID, CUHK03 and MSMT17 datasets. The experimental results show that our method is effective in occluded person re-identification
Track Defect Detection Based on Neural Network
Authors: Nan Zhefeng , Xu Yan , Cao Wenrui
Published: February 2021

The maintenance and inspection of the high-speed railway is the core problem of railway safety. To solve the problems of insufficient network depth and low defect extraction ability, an improved Unet network model was proposed. in view of the relatively small defect area, the attention mechanism is used to effectively suppress the background and highlight the significant features of the defect. The experimental results show that the improved Unet model improves the comprehensive detection accuracy of defect targets by 6% compared with similar segmentation models.
Research on Digital Marketing Communication Talent Cultivation in the Era of Artificial Intelligence
Authors: Lai Zhenli , Yu Lin
Published: February 2021

New technologies such as virtual reality, cloud computing, big data, augmented reality, motion-sensing interaction, and artificial intelligence has brought new changes to digital marketing communication talent cultivation. Students should be trained to master the knowledge of artificial intelligence applications and have a broad international vision.
TOR Anonymous Traffic Fingerprint Extraction and Recognition Based on Neural Network
Authors: Pu Tao , Wang Juan
Published: February 2021

The TOR anonymous communication system is an important means to protect network communication security and user privacy, but there are still criminals trying to destroy the confidentiality of the anonymous communication system through some special methods. Aiming at the abuse of the TOR anonymous communication system, this paper proposes a neural network-based anonymous traffic identification method, which uses a one-dimensional convolutional neural network for feature extraction, prediction and classification, and finally integration. In the experiment, nearly 100 websites were selected for the flow feature extraction and recognition based on one-dimensional convolutional neural network. The recognition accuracy rate is 87.5%, indicating that this method can effectively fingerprint TOR anonymous communication.
Intrusion Detection of Vehicle Based on Generative Adversarial Networks
Authors: Liu Ying Jiang , Wang Juan , Zhao Yang
Published: February 2021

In the process of applying deep learning to intrusion detection, in order to ensure the recognition accuracy of the model, a large number of data sets need to be classified manually, and then the model training is carried out after labeling. In practice, the efficiency of manual label designation for enough data sets is extremely low. This paper aims at the experimental data set encountered in the intrusion detection of intelligent network vehicles The problem of classification difficulty is proposed, and a method of vehicle intrusion detection based on Generative Adversarial Networks is proposed. Firstly, the vehicle driving data is collected, the collected data are put into the Generative Adversarial Networks for data classification, and the data set after training classification is used for model training. The experimental results show that the data classification can effectively improve work efficiency and reduce the resource overhead, which is practical in the application field.
Key Technologies and Applications In Intelligent Agriculture
Authors: Liu Qin , Yan Qing , Tian Jianying , Yuan Kexin
Published: February 2021

With the development of new generation information technology such as artificial intelligence, Internet of things and big data, traditional agriculture has changed and gradually changed to intelligent agriculture. In intelligent agriculture, sensor technology can collect all kinds of information in agricultural production, communication technology can transmit the collected information, and use big data analysis to guide agricultural production and agricultural product sales, The application of artificial intelligence makes agricultural production high-yield and high efficiency, and unmanned driving technology can realize automatic production and fine management. This paper elaborates the application of these key technologies in agriculture.
Research on New Media Content Production Based on Artificial Intelligence Technology
Authors: Wang Xiaocui , Liu Chuanlin , Qi Yue
Published: February 2021

At present, the main application of artificial intelligence in the media content production industry is to greatly reduce the application threshold, to a certain extent, to enable media workers to collect data from the low-end and produce visualization works for media terminals free from repetitive labor. Such as visual effects production, special effects design, video editing, subtitles sorting, data collection and comparison, image-text audio-video conversion, host broadcasting, and press release writing, are now increasingly completed through artificial intelligence programs. The working procedures of media content production are becoming more and more automated and intelligent.
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents
Authors: Chen Xuerun
Published: February 2021

In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such a boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis of the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of the reaction capability of specific agents.
A Hybrid Approach for Signal Modulation Recognition Using Deep Learning Methods
Authors: Fan Changjun , Wang Yufeng , Liu Ming
Published: February 2021

In this study, a novel signal modulation recognition framework has been proposed for automatically classifying eleven different modulation types with various SNR values. The framework employs both the raw complex-valued I/Q signal and its time-frequency description to represent the radio signal. And, a hybrid deep neural network is presented to recognize different modulation types from the representation data by leveraging the appealing properties of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Extensive validation of our scheme is performed on a large public dataset by comparing it with three existing 。 methods from literature, and our scheme yields quite promising results in terms of recognition accuracy.
Research of Attention-Based Bi-GRU-CRF for Slot Filling
Authors: Zhou Luwang , Huang Zhimin , Nie Ying
Published: February 2021

Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately.
Collaborative Filtering Algorithm Based on Improved Time Function and User Similarity
Authors: Zhang Weiguo , Zhou Xiran , Yuan Weixuan
Published: February 2021

As a typical representative of information filtering technology in the era of big data, a recommendation system is an important means to solve the problem of information overload. A collaborative filtering recommendation algorithm is one of the important technologies to realize the recommendation system, but the traditional collaborative filtering algorithm only considers the similarity of ratings between users. As the number of users and the number of items increases, it faces user interest drift and reduced recommendation Precision, and other issues. In this regard, a collaborative filtering algorithm based on improved time function and user similarity is proposed. First, considering that user interests will dynamically change over time, this paper introduces an improved time function in the traditional scoring similarity; secondly, considering the impact of the number of items evaluated by users on the calculation of similarity measurement, this paper takes the Pearson correlation coefficient weighted scoring into account; Finally, the fusion recommendation is based on the improved time function and the weighted Pearson correlation coefficient to improve the Precision of recommendation prediction. This paper conducts simulation experiments on MovieLens 100K and 1M data sets respectively. The results show that, compared with the traditional collaborative filtering recommendation algorithm, combining the improved time function and the weighted Pearson correlation coefficient can effectively improve the recommendation Precision.
Multilayer Convolution Sparse Coding for Expression Recognition
Authors: Chen Shuda , Wu Yan
Published: February 2021

Facial expression recognition is widely used in various research fields. For facial expression recognition problems, deep neural network methods have a complex structure and poor interpretability, while traditional machine learning methods have less plentiful diverse features and low recognition rates. Therefore, a new Multilayer Convolution Sparse Coding (MCSC) method is proposed for facial expression recognition. The MCSC method deeply extracts the salient features of the human face through a convolutional neural network. Furthermore, it uses a multilayer sparse coding to learn layer by layer to recognize different facial expression features based on sparse coding, which improves the recognition accuracy of facial expressions. Finally, the MCSC method was validated on three public facial expression datasets, i.e. JAFFE, CK +, and Fer2013. We also compared and analyzed 5 feature extraction approaches. The results show that MCSC has the best facial expression recognition performance in the comparison algorithm. Its accuracies of the three data sets reach to 90.8%, 98.2%, and 72.4%, respectively.
A Novel Multi-Agent Deep Reinforcement Learning Approach
Authors: Yin Dong , Zhao Zhe , Dai Yinglong , Long Han
Published: February 2021

Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks
Application Analysis Of Smart Wearable Devices in Students’ Physical Activity Under the Background Of Big Data
Authors: Li Mingxia , Sun Minghao , Wang Liangliang
Published: February 2021

With the rapid development of information technology, big data is gradually being popularized and promoted in various industries. Smart wearable devices have attracted much attention for their objective, accurate and continuous measurement of the amount of exercise. With the continuous improvement of intelligent equipment performance, large data analysis and intelligent wearable device technology can real-time capture the movement of the students in class data: trajectory, heart rate, speed, cost, etc., through data analysis, to monitor and intervene student’s sports activities in the class at the same time, can help the teacher more scientific system of the training plan, a more comprehensive understanding of the students in the class level and performance, enhance the level of student’s physical stamina and physical health and will bring far-reaching influence to sports research.
An Effective Algorithm for Mining Indirect Association Rules
Authors: Qiaoling Duan
Published: February 2021

In order to solve the problem that it is necessary to scan the database many times and produce unnecessary frequent itemsets in the process of mining indirect association rules, a new algorithm FPI-mine based on FP-Tree is designed to mine the indirect association rules in transaction database. Firstly, FP-Tree is constructed, and then the indirect item pairs and intermediate support sets of all frequent items are found. Finally, all the indirect association rules are obtained by mining algorithm. It can directly mine indirect association rules without generating all frequent itemsets. Finally, the effectiveness of the algorithm is verified by experiments.
Finding Subgroups of UAV Swarms Using a Trajectory Clustering Method
Authors: Gu Kongjing , Mao Ziyang , Qi Mingze , Duan Xiaojun
Published: February 2021

Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network Integrated trajectory clustering(NIT) to solve the trajectory clustering problem in a fast-changing and chaotic environment which requires a quick response, fault tolerance, and accuracy. The experiment results prove the flexibility and adaptability of the NIT method towards various demands and multi-dimensional data. Moreover, the algorithm proposed based on the method shows priority over the other three trajectory clustering methods(DTW, Fréchet distance, GMM) on the accuracy, and fault tolerance in clustering swarm trajectories. The method raised in this paper is an innovation to both multi-agent systems identification and trajectory clustering methods.
Improved Mask Wearing Detection Algorithm for SSD
Authors: Deng Houkang , Zhang Jin , Chen Lingyu , Cai Meiling
Published: February 2021

Coronavirus disease is seriously affecting the world in 2019. Wearing a mask in public places is a major way to protect people. However, there are few studies on mask detection based on image analysis. In this paper, an improved mask wearing inspection algorithm based on the SSD algorithm is proposed. The SSD algorithm is improved to add a face mask wearing detection task. Based on the original SSD model, the algorithm improves the mask wearing detection capability by introducing inverse convolution and feature fusion in combination with an attention mechanism to filter out the information to be retained. A dataset containing 3656 tensor images was created and manually labeled for network training. Experiments on this dataset show that the algorithm has good accuracy for mask wearing inspection.
Research on Load Forecasting of Charging Station Based on XGBoost and LSTM Model
Authors: Xue Mingfeng , Wu Lin , Zhang Qi Pei , Lu Ji Xiang , Mao Xiaobo , Pan Yongtao
Published: February 2021

At this stage, due to the increasing use of electric vehicles, the position of electric vehicle load scheduling in grid power scheduling is becoming more and more important. Effective electric vehicle power dispatching can balance the peak-valley difference of power dispatching, increase the power supply utilization rate of power grid dispatching, and reduce the power supply pressure of line transformer. The load forecast can describe the user’s electricity consumption habits in the next period of time, and can provide important data basis for power dispatching. This paper summarizes the research status of electric vehicle charging load, analyzes traditional charging load research methods, propose a charging load forecasting method combining XGBoost(Extreme Gradient Boosting) and LSTM (Long Short Term Memory Network), And use the data of a charging station in Jiangsu to verify the calculation example. The proposed method is based on the prediction results of the XGBoost model for feature engineering, extracting data features using phase space reconstruction techniques and statistical methods. In addition, training the LSTM model for load prediction. Based on the charging record data of domestic charging stations, this paper applies the artificial intelligence method to the charging load forecast of domestic charging stations for the first time. The charging station load forecasting method studied in this paper can support the regional load forecasting research of electric vehicles with high permeability, and further optimize power dispatching.
The research of Random Forest Intrusion Detection Model based on Optimization in Internet of Vehicles
Authors: Li Yalin , Li Fei , Song Jiaqi
Published: February 2021

Security model is the main means to protect the network information security of vehicle. Due to the rapid development of artificial intelligence in recent years, machine learning technology is also emerging in the field of Internet of vehicles security. The random forest model is a strong classifier and can prevent overfitting better than the decision tree model. However, only using the traditional random forest invasion detection model has some problems, such as: the model detection time is long, the false alarm rate is high, the ability of using platform transplantation is poor, etc. In this paper, it is optimized in a lightweight way to reduce the time consumption and improve the accuracy of intrusion detection in the vehicle networking intrusion detection model.
The Early-Warning and Inspection System For Intelligent Greenhouse Based on Internet of Things
Authors: Wang Liangliang , Jia Lianqin , Wang Jiamin , Zhou Qian
Published: February 2021

The early-warning and inspection system for the intelligent greenhouse is an inspection robot based on the Internet of things and equipped with various sensors and intelligent cameras with 4G or 5G communication functions, such as temperature and humidity, light, gas, and flame. Which uses the wireless network transmission to the cloud computing platform for data analysis, sending warning information through smartphones to enable farmers to real-time monitoring in agricultural greenhouse crop growth status, temperature and humidity, illumination, CO 2concentration, plant diseases, and insect pests, etc., to achieve automatic control, scientific planting and raising the yield and increase their income. This paper mainly analyzes the hardware and software structure of this early-warning and inspection system, data transmission and processing, and Android application, so as to promote the application of intelligent planting.
Research of Intelligent Temperature Measuring Robot System
Authors: Tian Jianying , Jia Lianqin , Mu Shile
Published: February 2021

With the outbreak of novel coronavirus pneumonia worldwide, a kind of epidemic prevention robot is urgently needed in society. The intelligent temperature measurement robot system mainly includes the robot’s high stability multi-attitude omnidirectional movement module, robot path planning and autonomous navigation module, robot environment information acquisition and recognition module, robot remote control module, and robot system integration. According to the design principle of the wheeled robot, high stability and multi attitude omni-directional mobile platform of temperature measuring robot are constructed; path planning and autonomous navigation of robot are realized based on sensor data fusion; temperature detection and remote care function are realized by thermal imaging sensor system by capturing the thermal radiation emitted by the human body, and based on open modular control system architecture. Based on the existing research foundation, the intelligent temperature measurement robot system is established by integrating the robot multi attitude omni-directional mobile system, robot path planning, and autonomous navigation system, robot environment information acquisition and recognition system, and robot remote control system.
Design of Climbing Robot for Power Transmission Tower Maintenance
Authors: Tian Jianying , Liu Qin , Yuan Kexin
Published: February 2021

Periodic maintenance of transmission lines is a necessary guarantee for stable power transmission and avoiding serious power accidents. In order to reduce the safety risk of the first person climbing the transmission tower and the last person going down the tower when climbing the transmission tower manually, this paper develops an auxiliary climbing robot for the maintenance of transmission tower based on the bionic climbing principle. It includes the modular mechanical structure of the main body lifting, obstacle crossing telescopic, terminal clamping, and body jacking, as well as airborne motion control system and hierarchical control system of upper computer monitoring terminal. The comprehensive test results of indoor and outdoor simulated transmission tower environment show that the robot system can effectively complete the high-altitude climbing of power tower, and ensure the follow-up tasks of hanging, dismantling, and maintenance of safety anti-falling device.
An Adaptive Double Area Page Replacement Algorithm for NAND Flash
Authors: Li Jiatong , Hu Huaixiang
Published: February 2021

In order to improve the hit rate of memory pages and prolong the service life of flash memo ry, an adaptive double area page replacement algorithm is proposed. The buffer is divided into page area and page cluster area. The initial size o f each area is set. The size of the area is dynamically changed according to the number of page hits in the two areas. The replacement unit of the page area is a single page. The CCF-LRU algorithm is used in page area replacement policy. When the page in the page area needs to be replaced, the rep laced page is put into the page cluster area. The replacement unit of page cluster area is the whole page cluster. When the page area cluster needs to be replaced, the dirty memory pages in the page cluster are divided into small memo ry pages with the same size as the flash memory pages, and only the dirty small memory pages are written back. According to the analysis of experimental results, compared with CF-LRU, CCF-LRU and FAB, the hit rate is increased by 9%, 8% and 11% respectively
Research on the new Method of Real Estate Integration Confirmation and Registration Based on small Program App
Authors: Feng Yougui , Xing Zhurong , Han Ze , Xie Jiying , Wei Yequn , Xu Yanming , Wang Lei
Published: February 2021

In order to solve the problem of repeated collection of ownership information in the process of real estate integration confirmation and registration, a collection system applet was specially developed, which changed from the original technical staff from door to door collecting ownership information to government-led and farmer cooperation. According to the simple and clear system interface, the farmers enter the ownership information, upload the photos of the right source materials, the homestead and the house photos, which solves the big problem that the ownership investigation must be repeated and the ownership information must be entered one by one, which has changed to a certain extent. The operation mode of traditional ownership investigation has greatly reduced the pressure of field work, reduced the cost of manpower and material resources, improved work efficiency, and protected the legitimate rights and interests of farmers.
Graph Node Strength Histogram Publication Method with Node Differential Privacy
Authors: Liu Wenfen , Liu Bixia , Xu Qiang , Lei Hui
Published: February 2021

The node differential privacy (node-DP) can be used to protect the private information of nodes and edges in the graph. In this paper, we propose an algorithm of publishing node strength histogram under node-DP, which could improve the accuracy of publishing the node strength histogram. In this algorithm, we use the Sequence Edge removal to reduce the sensitivity of query function and restrict the weight of edges to make the distribution of node strength denser. Furthermore, we use the histogram grouping algorithm Hierarchical Cluster Grouping to group the buckets to merge buckets with close values into one group. The experiments show that our algorithm maintains higher data utility than those traditional histogram publishing algorithms under the same privacy budget.
The Stability Study and Simulation of Soil Landslide Based on Bishop Method
Authors: Yang Min , Zhao Hua
Published: February 2021

Landslide is one of the most common disasters in nature and is a kind of slope failure. Many countries in the world suffered, because of its wide distribution, frequent occurrence and great harm. In China, the national economy and people’s lives and property suffer heavy losses due to the landslide disaster. In this paper, first of all, we used the Bishop method to theoretically analyze the stability of a landslide in Sichuan Province, and then we established a simulation model in Flac3D, the rock and soil mechanics calculation software developed by ITASCA company in the United States, to analyze the stability of the landslide under complex conditions. Finally, the results of theoretical analysis and simulation are consistent, which can provide ideas and methods for geological disaster prevention in the future.
Electrical conductivity of MoS2 thin films and graphite under continuous injection to protons
Authors: Burkhanov G S , Lachenkov S A , Kononov M A , Bashlakov A U , Prosvirnin D V
Published: February 2021

The electrical conductivity of thin films of MoS2 and graphite has been studied under conditions of continuous injection of low-energy protons (∼ 1-4 keV). The effect of a sharp increase (by 3-4 orders of magnitude) in the electrical conductivity of substances with a layered structure (MoS2 and graphite) at T ∼ 293 K has been established. The observed effect increases (from 2 to 10 times) as the temperature decreases from room temperature to liquid nitrogen temperature (T ∼ 77 K) and with increasing number of protons injected into the sample. The established temperature variation of electrical resistance is typical for materials with a metallic type of conductivity. It is shown that a thin surface layer, onto which protons penetrate, is responsible for the change in the electrical conductivity of MoS2 and graphite films.
Investigation of the preparation of W-Cr system powders from oxide compounds by magnesium vapors reduction
Authors: Kolosov V N , Miroshnichenko M N , Yu Prokhorova T
Published: February 2021

The process of producing W-Cr powders by the reduction of the oxide compounds mixture Cr2O3-WO3 and CaCrO4-CaWO4 with magnesium vapor in the temperature range 700– 800 °C at rarefaction of 5-15 kPa was studied. W-Cr powders with a specific surface area at the level of 34 m2·g−1 were obtained during the reduction of mixture of CaCrO4-CaWO4 compounds. The resulting W-Cr powders are characterized by a mesoporous structure.
Tungsten-rich Ni-W coatings, electrodeposited from concentrated electrolyte for complex geometry parts protection
Authors: Krasikov A.V. , Merkulova M.V. , Markov M.A. , Bykova A.D.
Published: February 2021

Annotation

The process of electrodeposition of nickel-tungsten coatings from a citrate electrolyte with an increased concentration of salts of the deposited metals has been investigated. Based on the results of studying the effect of pH, the ratio of the concentrations of nickel and tungsten salts in the electrolyte, the temperature of the electrolyte on the composition and current efficiency of the Ni-W alloy, optimal conditions have been determined that make it possible to obtain Ni-50% W coatings at a rate acceptable for industrial use. Evaluation of the properties of the coating showed that it is non-porous and has a microhardness of 600-640 HV.
Resistive switching kinetics of parylene-based memristive devices with Cu active electrodes
Authors: Matsukatova A N , Minnekhanov A A , Rylkov V V , Demin V A , Emelyanov A V
Published: February 2021

The temporal characteristics of the resistive switching process in parylene-based memristive devices with Cu electrodes are studied. It was found that the switching time of the structures is hundreds of nanoseconds at switching voltages less than 2 V. The median value of the estimated energy consumption does not exceed 3 nJ. Thus, it was shown that parylene-based memristors are effective in neuromorphic computing systems, including those trained by bio-inspired rules such as memristive STDP. The possibility of further reduction of the switching energies down to picojoules when the size of the memristors is reduced to 50x50 µm2 (in crossbar architecture) is noted. Biocompatibility and scalability of the devices is also promising in the creation of energy-efficient wearable systems. The obtained results can be useful for further study of parylene-based memristors, in particular, for developing models of their performance.
Features of the research of the structure and assessment of the quality of composite materials by means of non-destructive testing techniques
Authors: Yu Rusakov D , Chernushin V A , Kuryatin A A
Published: February 2021

In the present paper, features and problems of non-destructive testing (NDT) of composite materials with big inside quantity of pores have been discussed. Solutions for issues of NDT of this materials have been proposed.
Numerical calculation of laser welds nanocomposite formation parameters for solders based on proteins and carbon nanotubes
Authors: Ryabkin D I , Taritsyna N A , Morozova E A , Yu Gerasimenko A
Published: February 2021

Laser welding is an alternative method of biological tissues joining. One of the actual tasks of laser welding of biological tissues is to determine the weld depth by a noninvasive method during surgery. The paper presents a model for calculating the duration of the formation of welds depending on the density of laser radiation power and the depth of the joint. The calculations were performed for the laser beam diameter d = 1.1 mm, power density in the range from 10 to 25 W cm1, weld formation depth l in the range from 0.2 to 1 mm and solders based on aqueous dispersions of bovine serum albumin, indocyanin green and single-walled carbon nanotubes.
Hybrid nanostructures superconductor-ferromagnet for superconducting spintronics
Authors: Sidorenko A S , Morari R A , Boian V , Prepelitsa A A , Antropov E I , Savva Yu B , Yu Fedotov A , Yu Sevryukhina O , Vakhrushev A V
Published: February 2021

The work is devoted to the study of the processes of formation of multilayer nanostructures, their vacuum deposition, and the manufacture of a novel functional element of spintronics - superconducting spin valve, which is a multilayer structure consisting of ferromagnetic cobalt nanolayers separated by superconductor niobium films. Multilayer nanostructures are fabricated by magnetron sputtering on (111) silicon substrates at a temperature of 300K. The prototypes of the superconducting spin valve are prepared from multilayer nanostructures by the method of sharp focus reactive ion etching (FIB). Modeling was carried out using molecular dynamics methods.
Green synthesis of zinc oxide nanoparticles using Moringa oleifera l. water extract and its photocatalytic evaluation
Authors: Adam F , Himawan A , Aswad M , Ilyas S , Heryanto  , Anugrah M A , Tahir D
Published: February 2021

Green synthesis of Zinc Oxide Nanoparticles (ZnO-NPs) using Moringa oleifera L extract is successfully carried out. ZnO-NPs is precipitated out with the help of Moringa oleifera L water extract and thermally treated at 300, 350, 400, 450, and 500°C prior characterization. The prepared ZnO-NPs were characterized using Fourier Transform Infrared (FTIR), X-ray diffraction (XRD), and The Ultraviolet-visible (UV-Vis). The photocatalytic activity was determined based on its ability to decompose methylene blue in solution. FTIR analysis result shows the absorption band at 422-497 cm−1 which associated with stretching of Zn-O, and the intensity of O-H bond (3417 cm−1) decreases with increasing calcination temperature. The XRD spectrum shows a significant increase in electron properties and bond formation as the temperature increase. Calcination at 500°C results in the achievement of methylene blue degradation as a pollutant model of 80% after 60 minutes of exposure. This study shows that Moringa oleifera L. water extract can be used as a green alternative in ZnO-NPs synthesis and calcination temperature affects the photocatalytic activity of the prepared nanoparticles.
High sensitivity and resolution sensor plastic optical fiber for determining compressive and tensile tensor concrete applications
Authors: Arifin A , Rusyam N I , Lebang A K , Abdullah B , Tahir D
Published: February 2021

This research discusses sensors based on plastic optical fiber (POF) for concrete compressive tests and concrete tensile tests. Sensors are made of plastic optical fibers without coat attached to concrete surfaces or planted in concrete with straight, one bending, and two bending configurations. Sensors are attached to concrete with four configurations to find the best sensitivity and resolution values. The pressure on the sensor is related to the loss of power. The best sensor characteristic based on plastic optical fiber for concrete compressive test and the concrete tensile test is the sensor with two bending configurations that are planted in concrete. The sensitivity and resolution values in the concrete compressive strength test were 0.085 volts/kN and 0.047 kN. While the value of sensitivity and resolution of the concrete tensile test obtained is 0.082 volts/kN, and 0.048 kN. Sensor-based on plastic optical fiber has the advantage that is low cost and simple in measurement method that can be applied for monitoring the condition of the building structure.
Implemented preconditioning conjugate gradient algorithm on electrical resistance tomography to modelling structural geology
Authors: Kusmiaran A , Sanny T A , Nurrahmi S , Yanti F , Minarti
Published: February 2021

The development of a method to enhance resolution in imaging subsurface structural geology has continuously developed. The method of tomography field is electrical resistance tomography that is a powerful method for imaging subsurface structural geology. The electrical resistance tomography method is divided into two parts that are forward modelling and inverse modelling. The principle of forwarding modelling is to solve elliptic differential equation second order by finite volume method. The result of this part is the subsurface resistivity magnitude where it is used to model structural geology. For modelling inverse, the algorithm that has used to the model of subsurface structural geology is the Gauss-Newton method with an updated model that is preconditioning conjugate gradient. The ability of the electrical resistance tomography has tested to models of subsurface structural geology such us layering, and fault model. The results have revealed that this method able to model the subsurface of the structural geology, delineate the edge of the layer, and enhance the resolution of the model.
Production of protein hydrolysate from tofu dregs using the crude extract of bromelain from pineapple core (Ananas comosus l)
Authors: Bahri S , Hadati K S , Satrimafitrah P
Published: February 2021

Tofu dregs are one of the waste products from making tofu that still has the potential to be processed, because the protein content is relatively high, namely 23.62%. This waste has not been optimally utilized by the community unless it is used as animal feed. One alternative to utilizing tofu waste is processing it into specific products, namely producing protein hydrolysate using bromelain. The purpose of this study was to determine the best concentration of crude bromelain enzyme extract and hydrolysis time to obtain the degree of hydrolysis (DH) of protein hydrolysate in tofu dregs. DH of protein hydrolysate was determined with various concentrations of bromelain (8, 9, 10, 11, and 12%) (v/v) and variations in the hydrolysis time (120, 180, 240, and 300 minutes). The best degree of hydrolysis was 22.82% using 11% crude bromelain extract with a hydrolysis time of 120 minutes.
Satellite data receiving antenna system for pleiades neo observation satellite
Authors: Hestrio Y F , Soleh M , Hidayat A , Afida H , Gunawan H , Maryanto A
Published: February 2021

Pleiades Neo Satellite is a future optical remote sensing satellite designed for launch in 2020 with a very high spatial resolution of up to 30 cm and consists of 4 constellation satellites to continuing Pleiades-1A/1B constellation. Pleiades Neo capable of producing high temporal resolution (revisit time) in one day to collecting imagery at the same location and its performance claimed will increase up to three times when compared to the Pleiades-1A/1B constellation. LAPAN, as the main official provider in high-resolution optical satellite data in Indonesia, has planned to receive the Pleiades Neo data. This paper discusses the antenna system for receiving Pleiades Neo data by LAPAN’s remote sensing ground station with analyzing the main antenna parameter requirements if Pleiades Neo will be received on Zodiac or Viasat antenna. By calculating the Carrier to Noise Ratio (C/N) value both of antenna system and compared with Airbus Defense and Space minimum C/N requirement about 13.8 dB at 5-degree elevation. The simulation result is obtained C/N value 14 dB and 6.5 dB on Zodiac and Viasat antenna, respectively. Based on the result analysis, it is found that the Zodiac antenna system is recommended for the Pleiades Neo data reception system.
Effect of additive polarity on the stability of organic-inorganic hybrid CH3NH3PbI3 perovskite thin film
Authors: Firmansyah  , Kartini I , Sutarno  , Ruslan
Published: February 2021

Synthesis of organic-inorganic hybrid CH3NH3PbI3 perovskite thin film from Pb(NO3)2 precursor was carried out by using alcohol with various polarity (isopropanol, 1-butanol and 1-propanol) as the additive. Preparation of CH3NH3PbI3 perovskite thin film was conducted by firstly forming Pb(NO3)2 thin film as the film’s precursor using spin coating method at 2500 rpm for 30 s with DMSO as the main solvent and isopropanol, 1-butanol or 1-propanol as the solvent additives. Pb(NO3)2 thin film was immersed in methyl ammonium iodide solution for 30 min to produce CH3NH3PbI3 perovskite thin film. The effect of solvent additive polarity on the grain size of perovskite film was studied using electron microscope method. Stability of CH3NH3PbI3 perovskite thin film was studied by using XRD and SEM. The result showed that increasing polarity of the solvent additives resulted in decreasing on the crystallinity of Pb(NO3)2 thin film. Pb(NO3)2 thin film with low crystallinity yielded high purity of CH3NH3PbI3 perovskite thin film with grain size up to 2.45 μm and have good stability with 43.09% degradation percentage for 24 h.
Geoelectrical Method for Detecting the Limit of Liquid Waste Flowing Below the Surface at Piyungan Landfill
Authors: Jayadi H , Muin M R , Sabhan  , Meidji I U
Published: February 2021

Increasingly limited residential land in urban areas results in residents looking for and making settlements around landfills. This affects people’s lives in fulfilling their daily needs, clean water. To meet the demand for clean water, some residents use well water as the primary drinking water source. Judging from the condition of the residential areas located in the landfill area, there may be an effect of waste, especially liquid waste, on well water sources. For this reason, research is carried out using the geoelectric method by combining two configurations, namely Schlumberger and dipole-dipole. So that it can provide an overview of the boundaries of liquid waste contaminants in the soil, which are associated with conductive fluids that affect the quality of clean water from residential areas around the landfill. In this study, it was found that the fluid flow associated with liquid waste has a lateral movement to the right with a depth of 24.5 meters.
Analysis of sea level changes in Balikpapan Bay as basic data for strategic planning the new capital city of Republic of Indonesia
Authors: Putri M R , Sari T , Anwar I P , Mandang I , Setiawan A , Tatipatta W M
Published: February 2021

Studies from various sectors are needed regarding the plan to move the capital city of the Republic of Indonesia from Jakarta to Penajam Paser Utara. The aquatic ecosystem that needs attention is Balikpapan Bay which is located in the vicinity of these activities. One of the factors that influence this is global warming. Therefore, it is necessary to analyze the variability and trend of sea level rise in front of Balikpapan Bay. The first mode of Empirical Orthogonal Function (EOF) shows that sea level around Balikpapan Bay has a strong correlation with the Multivariate ENSO Index (MEI). This indicates that the sea level in the area is predominantly influenced by ENSO. A strong correlation between the first principal component and MEI was found to be around -0.85 in Balikpapan Bay (significance level> 95%). Based on sea level anomaly data for the last 20 years, sea level in Balikpapan has increased with a linear trend of 5.4 mm/year, with a decreasing trend due to El Niño and an increasing trend due to La Niña.
Classification of rainfall data using support vector machine
Authors: Sain H , Kuswanto H , Purnami S W , Rahayu S P
Published: February 2021

Classification problems can be based on cross-section or time-series data. In general, some of the characteristics found in time series data are data that are very susceptible to containing noise and outliers. In this paper, time series data-based classification was carried out using the support vector machine (SVM) method. The SVM method is the most popular binary classification technique in machine learning. The advantage of this method is that it can find a global optimum solution and always achieve the same solution for every running. Another advantage is that it can solve the over-fitting problem by minimizing the upper limit of generalization errors. SVM classification model performance can be seen from classification accuracy and sensitivity and specificity tests. The results showed that based on the level of classification accuracy used the SVM method resulted in an accuracy rate of the prediction results with training data of 96,3% and the accuracy of the prediction results with testing data of 90,08%. Therefore, the classification of rainfall data using the SVM method has a very good performance.
Synthesis of silver nanoparticles using bioreductors from clove leaf extract (Syzygium aromaticum) and test of its antibacterial activity
Authors: Kasim S , Dali S , Rahmah M
Published: February 2021

Synthesis of silver nanoparticles using clove leaf extract (Syzygium aromaticum) bioreductor and its antibacterial activity has been studied. Silver nanoparticles were synthesized using the green synthesis method with clove leaf extract (Syzygium aromaticum) as a bioreductor. Nanoparticles were analyzed and characterized by a spectrophotometer by UV-Vis, FTIR, SEM, XRD, and antibacterial activity. Antibacterial activity testing was carried out against Pseudomonas Aeruginosa and Bacillus Subtilis using the disc paper method. Silver nanoparticles were successfully synthesized by a reduction method using clove leaf extract (Syzygium aromaticum). The resulting nanoparticles have varying wavelengths in the range 420-435 nm and have non-uniform shapes with the resulting particle crystal size diameter based on the Debye-Scherer formula is 36.57 nm. FT-IR spectrum comparison of clove leaf extract and silver nanoparticles shows the contribution of the OH functional group in reducing silver ions which is indicated by its presence decrease in absorbance intensity in the area of wave number 3400 cm−1 after the formation of nanoparticles silver. Antibacterial activity of silver nanoparticles against Pseudomonas Aeruginosa and Bacillus Subtilis which showed that the inhibition zone diameter was 11 mm and 9 mm.
The investigation of self-absorption effect in cylindrical Ni-63/4H-SiC betavoltaic
Authors: Rahastama S , Agani I , Salim R , Teluma YC , Waris A
Published: February 2021

The limiting efficiency of betavoltaic battery can be affected by the self-absorption of radioisotope source. Moreover, this effect can be more detrimental for a low energy beta emitter such as Ni-63. The cylindrical design is known to have a benefit over rectangular one in response to the self-absorption effect. In this study, the model of betavoltaic was built under MCNPX which also capable to do many particle simulation based on Monte Carlo method. The interaction of beta particle and its average energy deposition in 4H-SiC semiconductor as the radiation-electricity converter was calculated, along with photonic emission due to the bremsstrahlung event. Basically, the average energy deposition shows a deterioration along with the addition of source mass thickness. However, an optimum peak was observed at about 0.1424 mg/cm2 source mass thickness in the N+ substrate average beta energy deposition. The cylindrical surface magnification also give a positive influence to the energy deposition. In addition, the absorbed dose in each layers of semiconductor was also investigated, and the results were consistent to the semi-empirical equation of self-absorption theory.