bluuebunny/crossref_metadata_2025 huggingface dataset:
Garbage detection and classification based on improved YOLOV4
Authors: Guo Dongwei , Cheng Lufei , Zhang Meng , Sun Yingying
Published: September 2021

Object detection is an important research task in computer vision. With environment problems becoming seriously, garbage detection has become a hot direction in object detection. Though great progress has been made in garbage detection, there still exists challenges for general-purpose detectors with no public datasets and low recognition accuracy rate. Based on YOLOV4 model, this paper proposes an improved YOLOV4 detector for garbage detection. Specifically, CBAM is added to the feature extraction network in order to better extract features in deep networks, and focal function is integrated into the loss function to improve class imbalance. When preparing the data, environment information and random information are added to pictures to simulate the distribution of garbage in real environment, and finally an urban household garbage dataset containing 47 classes of 45,910 images named TrashSet has been produced. Experiments results on TrashSet verify that our detector has a considerable performance, and mAP reaches 97.15%.
Multi-source image target detection technology based on salient feature fusion
Authors: Cui Yuyong , Wang Shengzhe , Gao Xinyi , Wu Zhongjian , Yong Yang
Published: September 2021

The ground target can be easily affected by the complexity of ground objects and photoelectric interference, so the accuracy of target detection in complex background is not always satisfying. To this end, joint target detection of Infrared/Visible image, IR/VI, is adopted, and the feature representation is achieved by applying the method of deep image feature extraction. According to the different imaging characteristics of the target, the feature fusion is carried out to form a unified target feature representation. As a result, target detection based on deep learning can be achieved by the model established by such fused feature. Experiments show that the proposed method can effectively improve the accuracy of target detection.
MDCount: A Lightweight Encoder-Decoder Architecture for Resource-Saving Crowd Counting
Authors: Meng Xiaolong , Ren Zhengfei
Published: September 2021

This work put forwards a lightweight encoder-decoder architecture MDCount designed unambiguously for real-time high-precision crowd counting with constricted calculation resources. The lightweight backbone network MobileNetV2 is tailored to decrease parameter numbers in an acceptable accuracy. The encoder-decoder architecture with atrous spatial pyramid pooling modules is proposed to recover the spatial and contextual information at multiple scales. Experiments on realistic and challenging datasets and comparing contemporary approaches demonstrate that our method MDCount accomplishes equivalent performance with smaller computation costs.
SMCN: Simplified mini-column network for visual place recognition
Authors: Huang Guanyu , Chen An , Gao Hongxia , Yang Puguang
Published: September 2021

Visual place recognition(VPR) is challenging because the places to be recognized are often affected by complex environmental changes. It is important for loop closure detection in SLAM. In recent years, a large range of approaches have been developed to address this challenge. Among these approaches, the use of biological heuristics and structural information have improved the VPR performance. In this paper, we attempt to improve the biological heuristic method MCN, and combine the available but often overlooked structural information intra-set similarity to propose our first algorithm SMCN. Furthermore, we propose a novel temporal filter that considers temporal continuity and combines it with SMCN to get our second algorithm SMCNTF. Evaluation of both algorithms on ten dataset combinations shows that, our best model SMCNTF has a maximum increase of 32% in average precision and at least a 100-fold increase in computing efficiency. Moreover, fewer parameters need to be tuned comparing with MCN. Generally, our algorithm is much better than MCN.
A Dynamic Method Based-matrix for Batch Updating Decision Regions of Hybrid Information System
Authors: Chen Jiajun , Yu Shuhao , Jin Ping , Ma Yan
Published: September 2021

Dynamics and complexity are the important characteristics of current information systems, and intelligent decision-making problem of complex information systems in dynamic environment is an important research content in the fields of intelligent decision-making. Decision-theoretic rough set model is an important model based on rough computing, and matrix is an effective mathematical tool, which was widely used in various fields of intelligent computing because of its easy representation and convenient calculation. In this paper, neighbourhood decision-theoretic rough sets(NDTRS) and the dynamic updating methods of decision regions of hybrid decision information systems were studied, and a dynamic method based-matrix for batch updating decision regions of hybrid decision information systems was proposed by introducing neighbourhood relation matrix and probability matrix to calculate the decision regions.At the same time, In the process of algorithm design, the incremental matrix is defined to updated the probability matrix, so as to improve the algorithm from the perspective of time complexity.Furthermore,the algorithms were described and analyzed in detail.Finally, the experimental results fully proved the effectiveness and feasibility of the algorithm.
Earthquake observation data grading and storage research
Authors: Zhu Kun , Liu Hui , Ji Yu , Li Zehao
Published: September 2021

With the constant development of contemporary science and technology and increasing popularization of the big data technology, the concept of “database” has been integrated into people’s daily work and life. The database system also plays an indispensable role in earthquake early reports. This paper briefly analyzes introducing the mature big data technology to earthquake observation and seeks graded storage of databases to ensure a faster and more accurate retrieval of data as well as timely reports of earthquakes.
Research and Optimization of Conflict Search Algorithm for Multi-Agent Path Planning Based on Incremental Heuristic
Authors: Li Yang , Wang Jun , Zhang Hualiang
Published: September 2021

Conflict Based Search(CBS) is used for multi-agent Pathfinding (MAPF) to enable each Agent to reach the target node. The CBS algorithm uses the heuristic algorithm A* search to calculate the MAPF solution, and the path planning uses forward search, which cannot explore the path of the unknown region. On this basis, this paper proposes that, in the case of unknown map and changing environment at any time, when encountering new obstacles, the information obtained from previous search should be used without completely replanning the path. Because of the idea of incremental programming, the number of reprogramming times and the number of affected nodes can be reduced. The optimized algorithm consumes less time and memory, and improves the efficiency of path planning.
A stacking-based classification approach to android malware using host-level encrypted traffic
Authors: Xue Zhixing , Niu Weina , Ren Xixuan , Li Jie , Zhang Xiaosong , Chen Ruidong
Published: September 2021

In recent years, smartphones have been developing fast. Android, a mobile platform convenient and open to all, has attracted more audience than any one of its counterpart. However, mobile devices are frequently attacked by malware, which calls to malware detection. Currently, we are lacking studies of Android malware detection based on ensemble learning. In this work, we propose a model to detect Android malware. The model takes the encrypted traffic that the malware generates as input. Through clustering, the model removes the third-party traffic and retains the purity of the first-party traffic. The model extracts traffic features to construct host-level traffic fingerprint and classifies the malware through stacking-based ensemble learning. We use the publicly available dataset CICAndMal2017 to build the classification model. This dataset successfully classifies malware into different categories. In the controlled experiments we use SVM and Random Forest models. The results show that our model is significantly more accurate in classifying malware than SVM and Random Forest models, with an accurate rate of 96.7% in the optimal condition.
Lithology recognition of complex carbonatite by deep learning
Authors: Li Luoning , Qu Honglve , Jiang Yanan , Zhang Kai , Kang Lei
Published: September 2021

The formation of carbonate reservoirs is affected by many factors such as sedimentation, diagenesis, and tectonic evolution. The rock composition and pore structures are complex, which brings challenges to the interpretation of reservoir lithology. Therefore, a novel approach of one-dimensional convolutional neural network architecture (1DCNN) based on the optimization of gradient descent algorithm for lithology identification is proposed. By fully combining logging physical parameters and vertical structure sequence context information, the deeper intrinsic rules between reservoir lithology and wireline logs can be found. With feature extracting of multi-scale deep feature extraction from multivariable wireline logs, the ability of wireline logs to express lithological features is improved, and the accuracy of lithological identification is further improved. In order to illustrate the prediction effect, the carbonate reservoir of the Majiagou Formation in Block 41-33 in Sulige Gas Field is taken as an example. The results reveal that 1DCNN improves the accuracy of carbonate lithology identification by 0.95% to 11.16%, which provides a new idea for the recognition of complex carbonate lithology.
Research status and hotspot analysis of literature metrology in artificial intelligence field
Authors: Li Kai , Zhang Jianye , Li Degao
Published: September 2021

It is of great significance to describe the innovation track of emergency management digital research through metrology for better grasping the theoretical system and subject frontier, and promoting the practice of emergency management digital. In this paper, artificial intelligence (AI) papers published in the world from 2010 to 2019 in the SCI database of Web of Science are taken as the research object to explore the influence of countries in the field of AI research and the current hot areas, so as to provide data support and decision-making reference for AI policy making. It is found that in the process of the development of the digitalization of emergency management from scratch, a novel and complete research system has been formed, which takes the digitalization of emergency management technology as the core and takes the digitalization of emergency management organization, the digitalization of emergency management environment and the digitalization of emergency management objects as the foundation. Further studies have found that digital frontier including emergency management in large data, mobile Internet, Internet of things, technology innovation, and AI as the representative of volunteers to digital citizens in emergency management, digital and digital community, represented by the main body of innovation, and to no one digital dynamic emergency management and the digitization of emergency management as a representative of management innovation. Finally, the study concludes that the digital integration of emergency management consists of two parts: risk information and emergency response.
Mapping performance comparison of 2D SLAM algorithms based on different sensor combinations
Authors: Qu Pengtao , Su Chen , Wu Hang , Xu Xinxi , Gao Sheng , Zhao Xiuguo
Published: September 2021

Currently, some existing SLAM algorithms were widely applied in the field of mobile robots. However, most SLAM algorithms were tested either by simulation or using offline bags storing messages, which ignored other contributing factors influencing mapping including the changes of position and speed of the robot during it autonomous movement. The aim of this study is to investigate the solution of constructing more accurate map with high quality sensors based on freely available SLAM algorithms. Comparisons and analyses of three 2D SLAM algorithms (i.e. Gmapping, Hector SLAM and Cartographer) available in ROS were conducted to map different environments in our work using different sensor combinations of a LIDAR, a Stereo camera and an IMU, respectively. The research showed that the Cartographer algorithm has advantage over two other algorithms on constructing well-defined and informative environmental maps with minimal errors. Also, the LIDAR scan is more suitable than the ZED scan as the input of mapping algorithms, and the Cartographer combining the LIDAR scan, ZED odometry and IMU is the best solution to map in all combinations.
Multi-objective discrete strength pareto evolutionary algorithm II for multiple-product partial U-shaped disassembly line balancing problem
Authors: Lu FaYang , Liu Peisheng , Qi Liang , Qin Shujin , Xu Gongdan , Xu Zhiying
Published: September 2021

In disassembly industry, the disassembly efficiency closely depends on a reasonable disassembly mode and an efficient disassembly line. In the current manufacturing, the profit of multi-product partial U-shaped disassembly-line-balancing (MPUD) is relatively low and the energy consumption is high. According to the characteristics of the MPUD problem, a mathematical model for maximizing profit and minimizing energy consumption of human-robot disassembly is established. To solve the MPUD problem, a multi-objective strength Pareto evolutionary algorithm II is developed and crossover and mutation operators are improved to promote the convergence and feasibility of the algorithm. We perform the experiments on a ballpoint pen and a washing machine to simulate the disassembly process and compare the proposed algorithm with the nondominated sorting genetic algorithm II and the multi-objective evolutionary algorithm based on decomposition. Experimental results show that the proposed algorithm has obvious advantages in the most important metrics.
YOLOv5s-FCG : An Improved YOLOv5 Method for Inspecting Riders’ Helmet Wearing
Authors: Wang Pengfei , Huang Hanming , Wang Mengqi , Li Bingjun
Published: September 2021

Manual inspection of riders’ helmets is time-consuming and labor-intensive, low in efficiency and small in coverage. Aiming at this shortcoming, this paper proposed an improved YOLOv5s-FCG(FourLayers, CBAM attention, GhostBottleneck) helmet wearing detection method based on YOLOv5 (You Only Look Once). Based on the smallest volume of YOLOv5s in YOLOv5 series, the network was improved, the shallow feature detection layer was added, the three-scale feature detection was changed to four-scale feature detection, and the up sampling was increased by four times. Add the CBAM attention module; Use lightweight GhostBottleneck instead of Bottleneck structures. The results in our experiments show that YOLOv5s-FCG raises the average detection accuracy (mAP) by 2.0% compared with YOLOv5s on the ourselves’ riding safety helmet data set and 1.5% on the NWPU-VHR-10 public data set. The proposed algorithm not only ensured the detection rate, volume, computation and number of parameters, but also improved the detection accuracy. And it had good adaptability and generalization ability in complex road environments such as poor light and small targets.
Application of data encryption technology in simulation software
Authors: He Shaoqing , Wang Qian , Li Xiaoting
Published: September 2021

Due to the late start of China's industrial simulation software, the current virtual simulation market is basically occupied by foreign products, and the emerging forces are accelerating to enter.In order to help China occupy a field of simulation market, promote the research and development of simulation software, and ensure the security and stability of simulation network operation,This paper analyzes the definition, classification and principle of data encryption technology, and discusses the application of some encryption technology in simulation software [1].
Word Sense Disambiguation Method Based on Graph Model and Word Vector
Authors: Sun Tongtong , Du Yongxing , Zhou Liyong , Niu Lijing , Li Baoshan , Gong Yanzhang , Dong Jianmin
Published: September 2021

As a basic research of natural language processing, word sense disambiguation (WSD) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.
Research on Model-based Abnormal Traffic Detection Method
Authors: Han Jiaxin , Wang Xiaowei
Published: September 2021

Information security has become a concern of all walks of life, and anomaly detection can protect information security, so anomaly detection has become a research hotspot. In this paper, the principles of four commonly used model-based anomaly detection methods, namely, depth-based, distance-based, density-based and deep learning-based detection methods are introduced and their research status is reviewed. Analyzed the characteristics of the four methods, and finally pointed out the future development trend of anomaly detection methods and gave a conclusion.
A Quantum Multi-agent Blind Signature Scheme
Authors: Chen Jingjing , You Fucheng
Published: September 2021

With the development of information society, people are increasingly concerned about the security problems caused by information leakage, and digital signatures play an irreplaceable and important role in the protection of information security, which replace traditional manual signatures and seals in the network environment and have important roles such as anti-forgery, anti-tampering, anti-replay, identifiable identity and confidentiality [1]. However, with the in-depth study of quantum mechanics, it has been found that the confidentiality mechanism of classical digital signatures is under serious threat. Since quantum computing is a parallel operation with exponentially increasing operational capability, a series of complex mathematically difficult problems can be rapidly overcome. Based on the above, this paper proposes a multiple agent blind signature scheme, which adopts the key technologies such as the Unitary transformation and Hadamard gate operation, and adds multiple agent signature to quantum signature to improve the efficiency of the protocol, and adds blind signature to improve the security of the protocol. At the same time, the scheme uses quantum key distribution to ensure its unconditional security, which is applicable in systems such as electronic voting and electronic payment, and has certain anti-attack capability [2].
Yield Prediction of Household Garbage Based on SARIMA and Exponential Smoothing Model
Authors: Zhong Peiwen , Chen Zili , Hang Chuxin , Wu Shuang , Mei Liangcai , Sun Hongbo
Published: September 2021

Refuse classification is a waste treatment method, which creates reuse opportunities and enhances recyclable value for domestic waste. Prolonging the use cycle of renewable resources is one of the cores of China's sustainable development strategy. Analyzing the total amount of household waste can obtain the characteristics of regional garbage growth so that governments can promote the garbage classification process intelligently. This article chose the household garbage monthly yield data as the time series and extracted its seasonal information. We used RStudio software to develop the multiplicative seasonal autoregressive integrated moving average (SARIMA) model with 2 Holt-Winters exponential smoothing models (additive and multiplicative) for comparison. Seasonal factor charts explain that the seasonal effect cycle of series is 1 year, and its period of increase or decrease is 6 months. SARIMA(0,1,1)(1,1,0)12 is the optimal model for data, which is better than Holt-Winters models. The prediction accuracy of SARIMA model is 94.31%. For the next year, the SARIMA model predictions show that the growth rate of the household garbage yield is 2.65%, which is in line with the actual situation. The results of this study are valuable for the planning of waste classification policy in recent years.
A robust registration algorithm for 3D point cloud data with scale stretches and outliers
Authors: Li Jinlong , Zeng Ni , Meng Jingan , Gao Xiaorong , Zhang Yu
Published: September 2021

Generally, the point cloud data obtained by 3D scanner cannot express the overall information of objects at a single time due to the limited field of view, so registration algorithm is needed to obtain the complete information. For the registration of point cloud data sets with large scale stretches and outliers, this paper proposed a new iterative nearest point algorithm named Improved-ICP. In the case of isotropic point extension, the iterative reweighted least squares method is constructed by incorporating a weight function to the minimum error function. In calculation, the weight function is equivalent to increasing the weight the point pairs, and the weights are obtained by an M-estimation criterion. In the paper, the initial registration was optimized to get global convergence for the algorithm, and compared the Improved-ICP algorithm with the ICP algorithm and the Scale-ICP algorithm to verify the effectiveness of the algorithm. To demonstrate the robustness of the Improved-ICP algorithm, we performed several comparative experiments with different scales and different noise between the Scale-ICP and the proposed algorithm in the presence of abnormal points. Experiment results illustrate the Improved-ICP algorithm has high accuracy and strong robustness to scale and abnormal points.
The drones configuration system based on linear programming with A-star intelligent algorithm
Authors: Lu Yizhuo , Jiang Xinxin
Published: September 2021

In this paper, We according to Australia on October 1, 2019 to January 7, 2020, took place during the fire, put forward a kind of drones configuration system, mainly used for the different terrain, different fire, under the premise of guarantee the safety and economy, determine the number of SSA drones and relay radio, so that the firefighters can better cooperate with EOC (Emergency Operations Center). Based on the satellite map, this paper classified the fire areas in Australia into four types, according to the performance and possible function of the drone, and determined the number of SSA drone and relay radio drone in the four areas with the largest fire area in Australia based on the different types of fire areas that have been classified, combined with drone performance, and using the idea of linear programming. Then, the A-star intelligent algorithm is introduced to the urban terrain where the hand-held bidirectional radio is limited, and the shortest path from the entrance point to the end point of the fire area is found out. According to the shortest path and the performance of the drone, the number of SSA drone and relay radio drone in this area is determined,and then the fires in Australia in recent 170 years are rated, then get a record of extreme fires in Australia over the last 170 years, and use the Poisson distribution function and Monte Carlo algorithm to predict the extreme fire scenarios in Australia in the next decade to determine the increase in the number and equipment budget of SSA and relay radio drones.Finally, this paper use ArcGIS software to eastern Victoria terrain simulation, after get the specific data, using drone configuration system determines the position of trunking radio drones, and use genetic algorithm to improve model, to get the position of trunking radio drone, the results said than before improvement, relay radio drone to reduce the number of location is more reasonable.
A study of individual identification of radiation source based on feature extraction and deep learning
Authors: Zhao Dongxing , Yang Junan , Liu Hui , Huang Keju , Li Xiang
Published: September 2021

Emitter identification technology can distinguish the types of radiation sources and identify the identity of emitter. It has broad application prospects in both military and civilian fields. The article mainly reviews the radiation source feature extraction methods for individual identification in recent years, and discusses the advantages and disadvantages of manually extracted features and the feature extraction based on deep learning. The technical difficulties of radiation source feature extraction methods are summarized with respect to the environment, the number of radiation sources, and the performance of algorithms, etc. Finally, the article points out the possible future development directions of individual radiation source identification.
Augmented mechanical properties of magnesium AZ91 reinforced with graphene
Authors: Yuwaraj K R , Kanagaraja K , Kumar Dadi Pavan , Reshmanth Bachu , Mukesh Bhavanasi Mohan Vamsi
Published: September 2021

Graphene has been impressing us in recent years by boasting some very spectacular things. Its outstanding mechanical properties made the material to be used in composites for amplifying the strength. Mechanical properties usually take precedence for magnesium alloy when it would be used as a structure material. The main focus of this project is to manufacture magnesium reinforced metal matrix composites using graphene nano powder through stir casting to enhance the mechanical properties. The uniform distribution of graphene nano powder and its large specific surface area per volume is embedded in magnesium matrix leads to increase in micro hardness, tensile solidity, fracture exertion of the composite. Compared with standard single crystal magnesium, the Mg AZ91 with 0.3 and 0.7 wt % graphene nano powder composite indicates improvement in mechanical properties. In addition to standard procedures for the tests and dissection of mechanical properties of synthesized composite, de-wintor invertor trinocular metallurgical microscopy, is used to investigate the phase segmentation, grain size and phase contrast. Additionally, Mechanical properties of synthesized composites of two different composition of graphene were compared.
Performance and evaluation of vegetable oil-based fluids as future cutting fluids in turning of duplex stainless steel
Authors: Arul K. , Rajaparthiban J. , Mohanavel V. , Sathish T , Subbiah Ram , Ravichandran M.
Published: September 2021

This experimental study article analyses the machinability of Duplex Stainless Steel when turned using various wet coolants, including as (Coconut, Mustard, Soyabean oil). Cutting speed, feed, depth of cut, nozzle inclination angle, pressure, and standoff distance are all input process factors. The output response parameters were considered such as thrust force, machining zone temperature, tool wear and surface roughness. In this experimental study, Taguchi technique was used to design the experimental runs under three different level combination of process parameters. The experimental result indicates that the turning performance has been improved at coconut oil condition over the other coolant conditions and it produces the uniform surface finish throughout the length of the workpiece.
Mechanical and water absorption behaviour of palm seed particles based hybrid bio-composites
Authors: Rajkumar K. , Vinoth N. , Santhosh Kumar R. , Sathish T. , Subbiah Ram , Ravichandran M. , Mohanavel V
Published: September 2021

In the recent decades, many scholars and researchers are interested in natural fibres today because they are inexpensive, widely available, and easy to process compared to conventional fibres. Due to environmental awareness and growing concern over the greenhouse effect, the construction, automotive, and packaging industries are also looking for eco-friendly materials that can replace conventional synthetic polymeric fibres. Alpaca fiber (AF), palm seed filler (PSF), and polypropylene (PP) were combined in this investigation to create composites that outperformed traditional polymer composites in terms of properties. The alpaca fiber/palm seed filler polypropylene (PP) composites were prepared using a conventional injection molding technique with fiber loadings of 35, 30, 25, 20, 15, and 10% and filler loadings of 15, 10, and 5%. (0, 5, 10, 15, 20 and 25 wt. percent). Polypropylene (PP)/Alpaca fiber/palm seed composites with a filler content of 20 wt. % were found to be more promising than PP/Alpaca fibre composites.
Additive manufacturing with composites of poly lactic acid (PLA)
Authors: Kishore R , Vinayaga Moorthy M , Gokul P.S , Mugilan  , Mugundhan
Published: September 2021

Additive manufacturing [AM] is a technology wherein the product is manufactured in a sequence of layer. AM has found a wide range of applications in today’s world as the need for a process of rapid prototyping is increasing. AM has its own place in the world of research and has been subjected to constant upgrades in process, machine setups and various other parameters. This study elaborately reviews about various methodologies in AM, its applications and its drawbacks. This work is concentrated to understand different methodologies adapted in additive manufacturing process and its process is been discussed.
Investigations on compressive strength of titanium diboride and graphite reinforced Magnesium Matrix Composites
Authors: Ravichandran M. , Subbiah Ram , Sathish T , Mohanavel V. , Arul K.
Published: September 2021

Magnesium matrix composites are widely used materials because of its excellent properties and low weight. In this work, the magnesium-based composites have been fabricated using powder metallurgy (PM) route. The compressive strength (CP) of the Mg-1wt.% Gr- 6 wt.% T1B2 composites were studied and reported. The effect of the compaction pressure (P), sintering temperature (T) and sintering time (h) on the compressive strength was analyzed. The increase in compaction pressure increases the mechanical properties of the composites. The optimum level of parameters for the high CS is that, A3B3C3 that is higher Pressure, temperature and time produced the high strength composite. ANOVA showed the compaction pressure is the highly influenced parameter with the F value of 180.85. Sintering time is the least contributing factor for the response for the present study.
Mechanical Properties of AlN and Molybdenum disulfide reinforced Aluminium Alloy Matrix Composites
Authors: Ravichandran M. , Mohanavel V. , Sathish T , Ganeshan P. , Suresh Kumar S. , Subbiah Ram
Published: September 2021

Aluminium based composites are extensively used in various applications because of its excellent properties. Aluminium alloy (AA7075) matrix reinforced with Molybdenum disulfide (MoS2) and Aluminium Nitride (AlN) have been developed via stir casting (SC) method. The mechanical properties such as hardness, tensile strength (TS) and compressive strength (CS) have been studied and reported. The increase in amount of AlN in the matrix improved the properties and decreased the elongation. The porosity % is high for the increase in quantity of the reinforcement particles.
Wear properties of waste silk fibre reinforced PLA bio composites using taguchi technique
Authors: Subbiah Ram , Krishna Kumar B. , Sathish T , Ravichandran M. , Mohanavel V. , Raja T.
Published: September 2021

In the last two decades, composites that are biodegradable and environmentally friendly are becoming more popular. In this paper, Biodegradable polylactic acid (PLA) biocomposites reinforced with waste silk fiber were fabricated using the hot molding technique. PLA bio-composites containing silk fibers had better wear properties, according to the findings. For superior properties, the fiber content of 30% by weight is ideal. A silk-based biodegradable composite with enhanced mechanical and wear properties can be designed due to this research. Taguchi’s L25 orthogonal array was used to study sliding speed (S), wear load (L), fiber loading (F), and abrasive distance (D). The ANOVA results found that the abrading distance was the most significant parameter, with load, sliding velocity, and fiber loading in order of importance.
Optimization on end milling operating parameters for super alloy of Inconel 617 by Taguchi’s L27 orthogonal array
Authors: Sathish T , Arul K. , Subbiah Ram , Ravichandran M. , Mohanavel V.
Published: September 2021

In recent days super alloys contributions were created more impact on the current industrial growth with various researches and developments. Among these super alloys in this experimental study considered on the subject of the by CNC milling machine used end milling operation parameters optimization on Inconel 617. The famous Taguchi method in the company of L27 orthogonal array is used here for the greatest results on the optimization consequences. There are the three considerations focused for the end milling operation such as rate of feed (FR), velocity of the cutting (CV) and depth of cutting (CD) with 27 combination of experiment testing. These considerations were optimizing based on the end product roughness on machined surface (ROMS) and rate of the material removal (RMR). These two responses associated parameters were separately and mutually optimized and corresponding consequences were evidently point out with a variety of plots and response diagrams. The greatest consequences of ROMS (0.208795 microns) and RMR (8.20895 mm3/sec) obtained at the testing number of 7 and testing number of 25 respectively.
Traffic controlling and monitoring using IoT
Authors: Vennila C. , Chandraprabha K. , Vijayaraj M. , Kavitha S. , Vimalnath S. , Kalaichelvi K.
Published: September 2021

The population is increasing the number of vehicles and the number of highways day after day; the time spent travelling, waste fuel, air pollution and related transport problems are also increasing. The greatest challenge for traffic administration authorities is therefore to monitor and monitor traffic. The related work has shown the Internet of Things and the integration of artificial intelligence to facilitate techniques for better urban and decision­making. This document creates a system model for IoT-based traffic information collection, processing and storing in real time. The aim is to ensure smooth mobility by providing roadside communications updates and rare incidents in real time. In particular, pre-alerting messages prevent and delay road congestion and peak hours in emergencies. The system also sends traffic updates from the database of administrative sensors. The system proposed evaluates the feature of the model and shows expected accurate results for vehicle detection and the minimum error in the estimates of occupancy.
Experimental Investigation of unused heat recovery using ORC cycle in a passenger car
Authors: Yuwaraj K R , Srinivasan S , Rajesh S , Arun B , Aftab Aatif
Published: September 2021

The internal combustion engines of automobiles generally operate around 25-40 percent conversion efficiency. The productivity of the engine can be improved by using the dissipated waste heat by the automobile cooling jacket. An ORC (Organic Rankine Cycle) unit can be installed to recover the waste heat in engine cooling jacket of a passenger car. The installation of this unit shall reduce part load from the engine by reducing the load acting directly on the alternator. The System shall increase the overall efficiency and also improves the fuel economy by using the waste thermal energy. A Thermodynamic cycle analysis and prototype fabrication of model has been considered for the experimental evaluation of the efficiency and effectiveness of ORC system. The cycle is considered using an Automotive Air-conditioning Scroll compressor as the turbine expander and also as an feed pump. Low temperature heat source from a heater is considered for the heat source in the boiler setup. The ORC turbine shall be coupled with and Electric Generator and then to the battery for recharging. The whole Setup is considered for mass production of ORC Units for cheaper price and light weight. Owing to the fact that this setup is a prototype the efficiency of the cycle can be improved further in order to careful adjustment of the critical parameters such as pressure, temperature, flow rate of the refrigerant of the thermodynamic cycle and the flow rate of the coolant according to the actual performance of the vehicle.
New approach to optimize groundwater usage using multi-criteria decision making techniques
Authors: Aparna P , Abhinaya V , Anbarasi D , Balaji V
Published: September 2021

Chronic pumping of water from aquifers leads to groundwater depletion, leading to an alarming scarcity of groundwater supply. Early detection of low aquifers levels can prevent the complete exhaustion of groundwater. We propose a Groundwater Optimization model using Multi-Criteria Decision Making (MCDM) techniques based on field values of bore wells like aquifer level, availability of nearby bore wells, and depletion to recharge ratio. In order to achieve higher accuracy than the existing models, we take both predefined values and on-site values of individual bore wells. The hardware components include a calibrated pressure transducer, water sensor, GSM module, Raspberry Pi, and IoT module. They are used in order to send the in-field data. When the aquifer level drops below the threshold level, an SMS alert is sent to the owner of the bore well. The threshold level is dynamically determined using the key steps of MCDM methods. The MCDM methods are implemented in Python code to determine and fix the threshold for the instance. The frequency of data logging is adjusted by studying the input patterns, which saves storage. When the water level drops below the threshold level an aquifer recharge technique is sent as a recommendation. The proposed optimization model has the potential to be applied to a larger geographical area in the future.
A Review on the significant classification of Additive Manufacturing
Authors: Shanmugam Ragavanantham , Ramoni Monsuru O. , Chandran Jishu , Mohanavel Vinayagam , Pugazhendhi Lakshmanan
Published: September 2021

Additive manufacturing (AM) implementation has advanced beyond prototyping towards becoming an extremely configurable manufacturing process with the illustrated ability to change conventional manufacturing in the future generations entirely. AM is completely based on Rapid Prototyping (RP), which helps fabricate critical and immense structure with 3D CAD software. The primary goal of this method is to create a product with optimal features such as compact, increased energy absorption, material reduction, insulation, and heat exchange. On the opposite, modeling and generating lattice structures and implementing different applications are all required. This review will get into the concepts of various classifications of additive manufacturing and its features.
Salen cobalt complex catalyzes the copolymerization of carbon dioxide and propylene oxide with high activity
Authors: Wang Wen-Zhen , Zhang Yi-Le , Shi Jun , Wang Li , Jia Xin-Gang , Li Lei-Lei , Zhang Kai-Yue
Published: September 2021

A series of Salen cobalt complexes have been successfully synthesized and they show high catalytic activity for the copolymerization of propylene oxide (PO) and carbon dioxide (CO2), yielding poly(propylene carbonate) (PPC). After optimizing the reaction conditions such as temperature, pressure and reaction time, the catalytic system achieved excellent conversion (99.47%) and showed high catalytic activity with an initial TOF up to 483.18 h−1. The content of carbonate chain of the PPC and head-to-tail connection are 88.92% and 86.79% respectively. PPC starts to decompose at 230°C and completely decomposed at 260°C. The Mn of the PPC is 109.898 kg/mol, and the PDI is 1.65295.
Preparation of praseodymium-doped La9.33(SiO4)6O2 apatite-type solid electrolytes and analysis of the conductivity mechanism
Authors: Chen Shi , Huang Zhiliang , Wu Changsheng , Huang Jiangsheng
Published: September 2021

In order to improve the electrical conductivity of lanthanum silicate oxide electrolyte (LSO), La9.33Pr
x
Si6O26+1.5x
(LPSO) solid electrolyte powder doped with praseodymium was synthesized at 600 °C by urea-nitrate combustion method with lanthanum oxide (La2O3) and Praseodymium nitrate hexahydrate (Pr(NO3)3·6H2O) as raw materials. It is shown that the Pr-doped LSO still have the typical apatite structure and occupy the LaII cation vacancy at the 6h position. Optimal sintering temperature is 1550 °C. La9.33Pr0.5Si6O26.75 reaches the highest conductivity (4.80×10−4 S·cm−1 at 600 ºC). The main factor that Pr-doped improves the performance of LSO is that Pr-doped that introduced more interstitial oxygen ions Oi* and increased lattice volume, so improved the transport efficiency of interstitial oxygen. The secondary factor is the decrease in the concentration of cation vacancy after Pr-doped, which reduced the negative defect stacking centers. Thereby, decreases space drag of interstitial oxygen transport which increases conductivity of LSO. The praseodymium doped to enhancement LSO conductivity mechanism that interstitial oxygen concentration-cation vacancies concentration-lattice volume composite enhancement mechanism was proposed.
Experimental Study to Investigate the Effects of Temperature Rise during Discharge on Li-ion Battery Degradation
Authors: Xu Bin , Wen Guangrui
Published: September 2021

Li-ion batteries with higher discharge C-rates show accelerated capacity fade rates. However, an inherent coupling exists between discharge C-rate and temperature as heat generation is inevitable during discharge. The effects of discharge C-rates on capacity fade under consideration of temperatures have not been investigated. This paper presents an experimental study that separates the temperature rise during discharge from the non-thermal effect of discharge C-rates. A design of experiment including two discharge C-rates (C/2 and 3C) and two controlled temperatures (controlled ambient temperature at 45 °C and controlled surface temperature at 45 °C ) were used to provide cycling conditions. In controlled ambient temperature tests at 45 °C, battery surface temperature increased by 5.2 °C at the discharge rate of C/2 and 20.6 °C at 3C. In controlled surface temperature tests at 45 °C, battery surface temperatures were maintained at 45 ±1 °C . Experiment results show that the battery capacity degradation was the joint contribution of the temperature rise and non-thermal effects. Temperature rise during discharge contributes to the battery capacity degradation, while the non-thermal effect of discharge C-rate dominates the capacity loss.
Using DID Model to Analyse the Impact of Vehicle Purchase Tax on the Sales of Alternative Fuel Vehicle
Authors: Ma J , Mayburov I
Published: September 2021

At present, countries in the world are paying more and more attention to the importance of green development and sustainable growth, and taxation plays a very good role in regulating it. In China, the role of taxation policy as a means of stimulating green consumption is becoming more and more significant. This paper starts with the vehicle purchase tax and uses the Difference in differences (DID) model to study its impact on the alternative fuel vehicle industry. The purpose of the research is to use the method of empirical research to test the impact of preferential vehicle purchase tax policies on the sales of alternative fuel vehicles using automobile production and sales data as samples. This research is helpful to enrich the research on preferential policies of vehicle purchase tax, and provides a good reference for empirical research on the impact of vehicle purchase tax on the sales of the automobile industry. Researched the impact of tax incentives for automobile purchase tax, and proposes constructive guidance with China’s specific situation. It has an important impact on promoting China’s further improvement of the tax system reform and the development of the alternative fuel vehicle industry, and also plays an active role in supporting macroeconomic transformation and development.
Vulnerability of the Belt and Road Initiative to External Water Dependency
Authors: Wang L P , Li Z H
Published: September 2021

The Belt and Road Initiative (BRI) was proposed by China, aiming to promote multilateral cooperation with the world. However, existing studies has shown the external water dependency of the Initiative on the rest economies (Non-BRI), which may enhance the vulnerability of production activities in BRI. To understand the vulnerability of the BRI to water scarcity in Non-BRI, this study evaluates the impacts of water scarcity in Non-BRI on BRI, by using a multi-regional input-output model. Results show that among all BRI, China, South Korea, Russia and India were most vulnerable to water scarcity in Non-BRI, especially Spain, Sudan, the United States and Australia. Water scarcity to Agriculture sectors of Non-BRI exerted widespread influences on the BRI trade system. This study analyses the vulnerability of the BRI induced by water dependency on Non-BRI by identifying critical nations and sectors, and informs stokeholds of the BRI to pay attentions on the water scarcity of their upstream to mitigate the supply chain risk, and thus achieve sustainable development.
Anatomical and Morphological Characters of Dianthus Barbatus
Authors: Abdushaeva Ya M
Published: September 2021

The study of the anatomical and morphological characters of varieties and hybrids of Dianthus barbatus L plants and the identification of decorative characteristics by the color of flowers and their decorativeness is relevant in the technology of their cultivation. We studied the height of the shoots and the size of the bush, the diameter and doubleness of the inflorescence, to maintain constant characters in cross-pollinated species, since these characters vary greatly and are not constant within the variety and hybrid. Dianthus barbatus L plants have several biological characteristics that give them advantages over other flowering species. The high potential for the formation of many seeds is accompanied by reliable methods of their reproduction, due to the seeds preserved in the soil. Dianthus barbatus L represent a combination of different shades in one inflorescence - from white to maroon. The impact of numerous, both natural and anthropogenic, unfavorable environmental factors can lead to a decrease in plant productivity, therefore we carried out a comprehensive assessment of resistance based on anatomical and morphological characters and ecological methods of influencing the physiological characteristics of Dianthus barbatus L.