bluuebunny/crossref_metadata_2025 huggingface dataset:
An Ultra-Wideband Substrate Integrated Coaxial Line Array Based on Silicon-Based MEMS Process
Authors: Zhu Yu , Li Xiaochun , Mao Junfa
Published: August 2024

In this paper, an ultra-wideband substrate integrated coaxial line (SICL) array based on silicon-based MEMS process is proposed for high-speed data transmission. A quasi-coaxial transition array is designed to connect the SICL array with grounded-coplanar waveguides (GCPWs). Moreover, a stepped-impedance matching structure is introduced to reduce the discontinuities and improve the transmission performance. For high efficiency design, a Bayesian Optimization (BO)-based approach is adopted to optimize dimensions of the proposed SICL array. The objective function is set to be the weighted combination of the bandwidth and the crosstalk of the proposed array structure to obtain wide bandwidth and low crosstalk simultaneously. With the proposed BO-based approach, iteration times can be reduced by approximately 99.9% compared with the parametric sweep method. A 40-channel SICL array is designed and fabricated under silicon-based Micro-electromechanical Systems (MEMS) process. Ultra-wideband of 110[Formula: see text]GHz is measured in each channel with the insertion loss less than 3[Formula: see text]dB and the return loss larger than 15[Formula: see text]dB. The couplings between two adjacent channels are no more than [Formula: see text]30[Formula: see text]dB. Good eye diagram and low bit error rate (BER) are achieved for each channel at data rate of 95[Formula: see text]Gb/s and hence the total data rate can reach 3.8[Formula: see text]Tb/s. The bandwidth density of the proposed SICL array is 0.164[Formula: see text]Gb/s/[Formula: see text]m, which is much higher than those of the conventional SICLs.
An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments using Self Attention Generative Adversarial Capsule Network
Authors: Kumaran T. Senthil , Muruganandham A. , Sobya D. , Mathapati Mahantesh
Published: August 2024

Cyber-physical system is one of the essential components of wireless sensor network (WSN). WSN conjointly senses, collects, analyzes and communicates data on recognized items at service part before transmitting this data to the owner of the network. Blackhole, Grayhole, Flooding, Scheduling are the common WSN attacks that rapidly damage the system. Intrusion detection systems, such as WSN have drawbacks, like less detection rates, high computation costs and high percentage of false alarms, because sensor nodes have limited resources. Therefore, a Self Attention Generative Adversarial Capsule Network as an intrusion detection scheme for WSN (SAGACN-ID-WSN) is proposed in this paper to address the aforementioned issues. Initially, the data are amassed from WSN-DS dataset. Then, the data are sent for pre-processing. The input data are preprocessed using the Guided Box Filtering (GBF) method. Then, the preprocessed data are provided to the Atomic Orbital Search (AOS), Tasmanian Devil Optimization (TDO) and Ebola Optimization Search (EOS) algorithms for feature selection. The attacks are divided into four categories: normal, Grayhole attack, Black hole attack, flooding attack, and scheduling attack. The proposed ID-WSN is implemented in Network simulator 2 (NS-2) utilizing the dataset of WSN-DS. The metrics, like accuracy, precision, recall, F-measure, ROC and computational time are analyzed. The proposed SAGACN-ID-WSN technique attains 25.5%, 20.12% and 20.7% high accuracy, 51.136%, 59.04% and 32.81%; higher precision, 2.292%, 1.51% and 3.915% lower error rate compared to the existing models, like Dataset for Intrusion Detection Systems in WSN (SLGBM-ID-WSN), multi-layer machine learning-based ID-WSN (NB-RF-ID-WSN), deep learning and entity embedding-base intrusion detection systems in WSN, respectively.
A Low Computational Complexity Family of Spline NLMS Adaptive Filter Algorithm
Authors: Tavakoli H. , Abadi M. S. E.
Published: August 2024

This paper presents novel and low computational complexity spline adaptive filtering (SAF) algorithms based on partial coefficients selection and adaption. They are addressed partial update SAF normalized least mean squares (PU-SAF-NLMS) algorithms. The PU-SAF-NLMS equations are established via the steepest gradient descent constraint to the proposed cost function. In the proposed algorithms, the filter coefficients are updated based on periodic, sequential, and selective rules, leading to reduction in computational complexity. Furthermore, the convergence speed of PU-SAF-NLMS algorithms is close to the conventional SAF-NLMS. Moreover, the convergence analysis of algorithms is studied. Ultimately, the performance of the presented algorithms is appraised through several experimental results.
A Novel Sinusoidal Oscillator Using Single Voltage Differencing Buffered Amplifier
Authors: Gupta Priyanka , Komanapalli Gurumurthy , Pandey Rajeshwari
Published: August 2024

In this paper, a voltage differencing buffered amplifier (VDBA)-based second-order sinusoidal oscillator (SO) configuration, which provides voltage output at low impedance, is proposed. The circuit employs single VDBA, three resistors and two grounded capacitors, which presume its easy monolithic integration with preferred circuit simplicity. The nonidealities of VDBA due to parasitic and tracking errors are taken into consideration to study the nonideal behavior of proposed SO. Mathematical modeling of phase noise is computed in the paper. Active and passive sensitivities are calculated for the proposed configuration and are found to be less than unity rendering circuit insensitive to component variations followed by phase noise study. The functionality of the proposed topology is validated through cadence virtuoso ADE spectre tool at 180-nm technology node. Experimental verification is executed through commercially available ICs AD844 by good results. The total harmonic distortion (THD) is found to be below 3.5%. The observed frequency of oscillation is found to be in close agreement with the theoretical values. The PVT and Monte Carlo statistical analyses are also carried out to confirm the sensitivity of the proposed SO.
A New Design of an Optimized Informer Wind Power Prediction Model Utilizing Wind Turbine Health Assessment
Authors: Xie Xin , Huang Feng , Peng Youyuan , Zhou Wenjuan
Published: August 2024

Wind power prediction is of significant value to the stability of the power grid. Employing the Informer model for wind power prediction yields better results than traditional neural networks, yet issues such as slow speed and insufficient accuracy persist. By utilizing a health assessment algorithm to optimize the Informer model, both prediction accuracy and speed can be concurrently enhanced. Initially, a health matrix is obtained by performing a health assessment of wind turbines based on operational data. Subsequently, this health matrix is used to optimize the encoding method of the Informer, improving prediction speed. Simultaneously, the decoding method, embedding vectors and prediction process of the Informer are refined to increase prediction accuracy. Finally, conventional Informer models and optimized Informer models are tested and compared using four distinct wind power datasets. The results indicate that the optimized Informer model achieves an approximately 15% increase in prediction accuracy and about a 100% increase in prediction speed.
A Deep Learning-Based Recommender Model for Tourism Routes by Multimodal Fusion of Semantic Analysis and Image Comprehension
Authors: Li Feifan , Zhang Chuanping
Published: August 2024

Tourism recommendation systems have tended to become popular in recent years. Due to the fact that tourism content is generally with the format of multimodal information, existing research works mostly ignored the fusion of various feature types. To deal with this issue, this paper resorts to multimodal fusion of semantic analysis and image comprehension, and proposes a novel deep learning-based recommender system for tourism routes. First, semantic analysis under tourism route search is conducted, in order to complete destination selection and process selection. Then, image comprehension of overall tourism route planning is conducted by establishing an end-to-end object recognition model. Finally, the previous two parts of characteristics are fused together to formulate an integrated recommender system with multimodal sensing ability. This thought is expected to bring a stronger ability for tourism route discovery. Empirically, operational efficiency and stability analysis are carried out on real-world data to evaluate the performance of the proposal. The experimental results show that it can achieve significant improvement in tourism route recommendation, can accurately capture user preferences, and can provide travel suggestions that meet user requirements.
A Three-Party Game Based on Trust for IoT Task Delegation in an Untrusted Environment
Authors: Li Tong
Published: August 2024

With the rapid development of technologies and applications, extending from trusted environments to untrusted environments is the development trend of Internet of Things (IoT) applications. Due to the limited resources of nodes and the rise of edge computing, more and more IoT applications are required to have task delegation functions. Delegating IoT tasks in untrusted environments has to solve two problems: First, how to evaluate the trustworthiness of nodes so that requesters can delegate tasks to trusted providers; the second is how to ensure the successful implementation of the task delegation between the requester and the provider. While trust models are often used to solve the first problem, the lack of ability to describe transactions between the requester and the provider makes them unsuitable for solving the second problem. In recent years, game theory has been more applied to task delegation. By setting appropriate utility functions, game theory can ensure the successful implementation of task delegation. However, since it is unable to evaluate the trustworthiness of nodes, game theory cannot solve the first problem. This paper combines the advantages of trust models and game theory to construct a game of three-party task delegation based on trust to explore a solution to problems of IoT task delegation in an untrusted environment. Finally, aiming at the problem that setting essential parameter values of trust models is empirical and experimental, this paper theoretically discusses how to set these values in an untrusted environment.
A Biometric Features-Based Watermarking Encryption Approach for Secure Authentication of Visual Images
Authors: Xu Xiangyang , Song Yu , Alharbi Abdullah , Tolba Amr
Published: August 2024

Due to the uniqueness of biometric features, it remains practical in generating cryptographic keys. In this study, a region-based encryption method is proposed, which can avoid the attacks on the whole encryption such as block attack and frame insertion attack. This method can protect important areas while encrypting images so that the image can maintain recognition and credibility even when attacked. It has certain universality and scalability, and can be applied to a variety of scenarios and systems. First, biometric features are introduced to extract keys from fingerprint key images through grayscale segmentation and gradient segmentation. Then, a watermarking addition algorithm is completed based on single encryption and reversible exchange encryption using hybrid vectors, and the image is encrypted using encryption algorithms. Next, the generated biometric key is embedded into the encrypted image, and a complete watermark encryption model named BKE is established by adjusting the singular value decomposition algorithm. Finally, invisibility, decoding accuracy, and stability analysis are completed based on publicly available data. The experimental results show that this method has high effectiveness and feasibility in image security authentication, and this method improves the security and authentication accuracy of images. This method has broad application prospects and can be used for image security authentication in various fields.
A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy
Authors: Hu Hui , Peng Long , Li Xiaoqin , Chen Ying , Yan Jiande , Guo Peng , Cheng Bozhi
Published: August 2024

This paper proposes a novel L1 neural network (NN) adaptive control strategy to address the challenges posed by the non-affine nonlinear characteristics of wind turbines operating at high wind speed. These challenges complicate the design of control inputs and the issues of ensuring system transient performance due to unmodeled dynamics and external disturbances in variable-pitch control. This strategy comprises an estimator, control laws, and adaptive laws. The L1 adaptive controller ensures rapid adaptation and robustness of the system, and the NN is utilized to approximate the ideal implicit controller. This approach guarantees the uniform boundedness of system transient responses without the need for wind speed measurements. Finally, the new strategy proposed in this work is compared with traditional NN adaptive and PID control methods through simulation experiments, demonstrating enhanced robustness and superior transient performance.
MEDANet: More Efficient Dual Attention Network for Scene Segmentation
Authors: Ouyang Pan , Yao Xiaoguo , Huang Zhijian
Published: August 2024

The dual attention module is a potent semantic segmentation technique renowned for its capabilities, yet it often faces significant computational demands and GPU memory usage. To tackle these challenges, we introduce an advanced dual perception network comprising two modules: A streamlined Multi-scale Efficient Position Attention Module (MEPAM) and an optimized Efficient Channel Attention Module (MECAM). MEPAM incorporates multi-scale global average pooling into the Position Attention Module (PAM), substantially cutting computational overhead and memory consumption without compromising performance. Meanwhile, MECAM integrates compressed convolutions into the Channel Attention Module (CAM), improving segmentation accuracy and inference speed compared to conventional methods like DANet. Our approach underwent comprehensive evaluation on a semantic segmentation benchmark dataset, showcasing superior performance. For instance, on the Cityscapes dataset, our method achieves an IoU of 82.2%. In terms of efficiency gains, MEPAM operates nearly 1.97 times faster than the standard PAM module on GPU, while requiring 7.55 times less memory with a [Formula: see text] input. Similarly, MECAM achieves approximately 2.2 times faster processing than CAM, while cutting GPU memory usage by 7.53 times. This innovative dual perception network not only enhances segmentation accuracy and speed but also addresses the computational challenges associated with traditional dual attention modules.
Robot Manipulator Minimum Jerk Trajectory Planning Based on the Improved Dung Beetle Optimizer Algorithm
Authors: Ma Haohao , As’arry Azizan , Cong Miaomiao , Delgoshaei Aidin , Ismail Mohd Idris Shah , Ramli Hafiz Rashidi , Wu Xuping
Published: August 2024

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (IDBO) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the IDBO algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance.
Two-Dimensional Ballistic Correction Decision-Making Based on Constrained Reinforcement Learning
Authors: Lei Xiaoyun , Zhang Lin , Zhu Lihua
Published: August 2024

In response to the poor decision-making caused by aerodynamic coupling and model linearization in the trajectory correction model, it is proposed to use a reinforcement learning method to establish a Constrained Markov Decision Process (CMDP) model between action prediction and action constraint. Taking a classical two-dimensional trajectory correction projectile for study, the cost function within constraints of thruster violations is built, so that the sequential prediction is transformed to solve the Constrained Reinforcement Learning (CRL) problem. Accordingly, a method combined with the policy gradient algorithm based on Lagrange operator with the model prediction algorithm is proposed. The test results show that the trained optimal policy model achieves a probability of over 65% and the correction projectile falls within a radius of 5 meters from the preset target point (CEP [Formula: see text]5[Formula: see text]m). Simulation examples have verified the decision-making of correction action sequences autonomously using the trained model, resulting in correction errors of 1.79 1.1 and 0.42[Formula: see text]m in the [Formula: see text] and z directions, respectively, demonstrating high correction accuracy and autonomous decision-making capability.
Research on CNN-BiLSTM-SAM and Multimodal Feature Fusion for Speech Lie Detection
Authors: Zhou Yan , Pan Xinyu , Bu Feng
Published: August 2024

Lying state detection is a typical psychological calculation problem with significant spatiotemporal dynamic changes. However, most existing detection methods have not fully considered the dynamic changes in psychological states, and the distribution information of time series in multimodal spaces has not effectively utilized. The current detection systems lack adaptive fusion methods for multimodal features and it is difficult to extract their spatiotemporal dependencies. Therefore, a novel speech lie detection model was proposed that combines a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) neural network and multimodal feature fusion of Spatiotemporal Attention Mechanism (SAM). CNN has the ability to extract local spatial features, while BiLSTM can handle long sequences and long-term dependencies in bidirectional information flows, and the contextual information in sequences can be captured. The proposed model combined the short-term stationary characteristics in time and the diversity of semantic environments in space, and introduced SAM to fuse multimodal features of temporal and spatial dependencies as feature vectors for the detection model of lying psychological states. The simulation experiment results on the Open-source Real Life Trial lie database show that the average lie detection rate reaches 88.09%. In general, the proposed speech lie detection model has a significant detection accuracy improvement compared to the existing lie detection models.
An Image Text Sentiment Analysis Method Using Bimodal Attention Mechanism
Authors: Zhang Huchao , Ai Guojin
Published: August 2024

Aiming at the existing multimodal image sentiment analysis methods that suffer from insufficient text feature extraction, redundant independent modal features, insufficient analysis of semantic relationship between image and text data, and insufficient fusion, a multimodal image sentiment analysis method that integrates RoBERTa, Res-ViT and bimodal attention mechanism is proposed. By utilizing RoBERTa model to segment the input text, encode and extract high-level semantic features, local and global features of the image are obtained by using Res-ViT, and finally, image and text features are deeply fused to interact with each other by bimodal attention fusion. The model comprehensively mines the text and image features and deeply fuses the text–image features to interact, which enhances the accuracy of sentiment classification. The experimental outcomes demonstrate that the suggested RRMGA can enhance the accuracy of sentiment classification by attaining ACC and [Formula: see text] values of 0.7164 and 0.7042 on the MVSA-Multiple dataset, and 0.7519 and 0.7428 on the TumEmo dataset, respectively. These values are significantly higher than those of a number of other state-of-the-art multimodal image sentiment analysis approaches.
MAHR: A Multi-Application Hybrid Reconfigurable Mechanism for Energy-Efficient Chiplet Interconnection Network
Authors: Cao Zhipeng , Mei Bo , Liu Qinrang , Pei Xue , Wan Zhiquan , Wang Caining
Published: August 2024

The use of multi-core chiplet systems is a promising technology for efficiently running large-scale hybrid applications, offering significant cost and performance advantages over the traditional 2D integrated circuits. However, incorrect task mapping and architecture design can lead to high power consumption and degraded performance. Current solutions mainly address these problems for 2D monolithic chips, which cannot be directly applied to 2.5D chiplet systems. To this end, this paper proposes a Multi-Application Hybrid Reconfigurable (MAHR) mechanism for task mapping and interconnection network construction, aiming to reduce energy consumption while optimizing communication latency. MAHR consists of three steps. In the first step, a Modified Adaptive Large Neighborhood Search (MALNS) algorithm is designed to solve the Chiplet Hybrid Applications Mapping Problem (CHAMP), allocating computational resources to application tasks; in the second step, the interconnection network is rebuilt using the results from the first step. This is accomplished through a novel reconfigurable interposer router that allows packets to bypass intermediate nodes; and in the third step, the results obtained in the second step are post-processed by checking all interposer nodes, restoring the router configurations of the nodes that have conflict links, and power-gating the unused routers. Compared to existing methods, simulation results demonstrate that MAHR reduces system power consumption and improves performance with little overhead in both synthetic and real-world scenarios. On average, MAHR achieves a 6.11% improvement in performance and an 11.3% reduction in power consumption, while increasing its area overhead by only 5.88%.
Efficient Hardware Implementation of Spiking Neural Networks Using Nonstandard Finite Difference Scheme for Leaky Integrate and Fire Neuron Model
Authors: Venkateswara Reddy K. , Balaji N.
Published: August 2024

Continuous fire models are not suitable for the implementation of hardware units for applications and hence, suitable discrete versions need to be selected. Moreover, the nonlinear components in the neuronal equations reduce system performance (in the case of frequency and number of resources). This research paper focuses on implementing efficient Spiking Neural Networks (SNNs) using Field-Programmable Gate Array (FPGA), with a specific emphasis on the Leaky Integrate and Fire (LIF) neuron model. Its objective is to optimize the mathematical equations of the LIF model by approximating nonlinear functions. This approach enables the development of a simple, cost-effective and high-speed design. Existing LIF Neuron Hardware Blocks (NHBs) are based on the approximation of continuous models by standard difference schemes such as the Euler method or R–K method etc. Mathematically, such approximations do not exactly represent all dynamics of continuous systems. There are good approximations for small step sizes but they behave oddly when the approximation step size increases. Hence, the corresponding discrete, digital versions are not suitable for applications in all cases. This paper utilizes a Nonstandard Finite Difference (NSFD) scheme for the hardware (FPGA) implementation of the exact model of LIF-based NHB that works for all step sizes. The model presented here has a speed of 438.686[Formula: see text]MHz which is more than other existing models presented in this paper. It is multiplier-less, unlike earlier models. Further, it is implemented for SNN for basic pattern recognition and established that the proposed model works properly for given patterns. The system was evaluated using large datasets such as MNIST handwritten digit recognition, achieving a classification accuracy of 97.8%. Additionally, it underwent testing for COVID-19 chest CT scan image classification, demonstrating an 84% accuracy rate which is 6% more compared to existing Spiking Neural Networks (SNNs).
Attribute Value Weighted Averaged One-Dependence Estimators with Kullback–Leibler Divergence
Authors: Zhu Changjian , Chen Shenglei , Ke Huihang , Zhang Chengzhen
Published: August 2024

The Averaged One-Dependence Estimators (AODE) algorithm is an improvement of the naive Bayes algorithm, which allows all the attributes dependent on one common attribute, called parent attribute, thus forming One-Dependence Estimators (ODE). The classification probability is estimated by averaging the conditional probability of the ODE. When there is a dependency relationship between attributes, the AODE algorithm can better capture these relationships, thus improving classification performance. The AODE algorithm treats the parent and child attribute values equally in different ODEs. However, the parent attribute value and the child attribute value in different ODEs have different importance for classification. In this paper, two attribute value weighted AODE based on Kullback–Leibler divergence are proposed, one is parent attribute value weighted AODE, and the other is child attribute value weighted AODE. Comparative experiments were carried out with 30 datasets in the UCI database, and the experiments indicate that the performance of the parent attribute value weighted AODE algorithm with Kullback–Leibler divergence is significantly better than the original AODE algorithm, and its performance is also better than the mutual information weighted AODE algorithm.
Discrete Cosine Transform-Based Embedded Application Design for Hourly Temperature Forecast
Authors: Yang Zong-Chang
Published: August 2024

The air temperature is a vital measuring variable widely applied in science and engineering as it is considered one vital part of the lives of plants and animals. From the point of view of time series analysis and data-driven modeling, changes in daily 24[Formula: see text]h air temperature can be described as one time-series data. Compared to relatively complicated models, simple, efficient and of even better performance are advantages of data-driven-based concise modeling methods, especially for embedded applications which usually have limited resources such as low RAM and low processing power. This study takes the perspective of data-driven modeling and time-series analysis to present one succinct and efficient solution based on discrete cosine transform (DCT)-extended modeling for the hourly air temperature forecast (HTF) in engineering and embedded applications. Characteristics of the DCT lie in optimal de-correlation and energy compaction. Orienting upon extended-DCT modeling, one concise least-squares (LS)-extended DCT predictive algorithm for the HTF is introduced. Then one Mbed-based embedded system design utilizing the proposed LS-extended DCT predictive algorithm as well as theoretical analyses and fast computation of arbitrary length DCT is presented. Verification results indicate the suitability of the proposed predictive algorithm. Only the temperature data without other parameters being straightly employed in forecast modeling is one major advantage of the proposed data-driven DCT-based HTF method, which benefits it to be suitable for engineering and embedded applications.
A Collaborative Neural Network-Based Automatic Object Recognition Method for Environmental Space Under Virtual Reality Scenes
Authors: Li Libo , Alfarraj Osama , Tolba Amr
Published: August 2024

The widespread application of virtual reality has made automatic target recognition in environmental space particularly important. However, traditional image recognition methods faced challenges due to the complex and diverse environmental characteristics in virtual reality scenes. It is easily influenced by occlusion, noise, and other factors. To deal with the issue, this paper utilizes the combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and proposes a collaborative neural network-based automatic object recognition method for environmental space under virtual reality scenes. Specifically, CNN is mainly responsible for feature extraction of environmental scene images, while RNN is used to model the spatial relationships of image sequences. Then, a data augmentation method is introduced in virtual reality scenarios, which can generate more training samples on a limited dataset, thereby improving the model’s generalization ability. Meanwhile, in order to address issues such as occlusion and noise, a multi-scale feature fusion strategy is further developed to improve the robustness of target recognition by multiscale feature fusion. Through experimental verification using actual data collected in virtual reality scenes, the results show that the proposed method has achieved excellent performance and strong adaptability compared with traditional methods.
Enhancing Road Traffic Prediction Using Data Preprocessing Optimization
Authors: Garg Tanya , Kaur Gurjinder , Rana Prashant Singh , Cheng Xiaochun
Published: August 2024

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm’s variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications.
A bracket polynomial for virtual links
Authors: Kim Joonoh
Published: August 2024

In this paper, we describe a method of making an invariant of virtual links, generalizing Jones–Kauffman polynomial of virtual links.
INVESTIGATION OF X-RAY EMISSION FROM LASER PRODUCED ALUMINIUM ANTIMONIDE PLASMA
Authors: ASHRAF SIDRA , KHALID SIDRA , SHAHEEN SAIRA , RAZA AHMAD , RIAZ SAIRA
Published: August 2024

Lasers are integral to numerous applications in our daily lives, with one of their notable uses being the ablation of materials to generate plasma, which in turn produces X-rays with a variety of applications. This study investigates the generation of hard X-rays from Aluminum Antimonide (AlSb) plasma, induced by a Q-switched Nd: YAG laser operating at 1064[Formula: see text]nm with a pulse duration of 9–14[Formula: see text]ns and a peak power of 1.1[Formula: see text]MW. The AlSb target was selected due to its unique electronic structure and high atomic number, which are advantageous for efficient X-ray generation. At a tight focus, the laser produced a spot size of 11.8 micrometers, achieving an intensity of 1012 W/cm[Formula: see text], to study the transmission of hard X-rays both in a vacuum environment with a pressure of 10[Formula: see text] torr and in air at atmospheric pressure. A Photo Multiplier Tube (PMT) was employed to detect the transmitted hard X-rays, which were filtered using a 10 [Formula: see text]m-thick Al filter. The fundamental mechanisms behind the emission of hard X-rays from laser-induced AlSb plasma were explored. Plasma plume expansion and the energy of the emitted radiation were found to depend on the number of laser pulses and the ambient pressure. The intensity of the plasma plume was observed to be inversely proportional to the ambient pressure and directly proportional to the number of laser pulses fired. Plasma confinement under high pressures was also examined. Time-resolved studies of the hard X-ray emission contributed to the analysis. The relationship between different laser shots and the current, voltage, and energy of the hard X-rays, as well as their inverse proportionality to ambient pressure, was quantified. At an ambient pressure of 10[Formula: see text] torr, the dynamics of the AlSb plasma plume were observed. The surface morphology of the laser-irradiated AlSb target was also analyzed, revealing unique properties pertinent to X-ray emission.
PERFORMANCE ANALYSIS OF AISI 4340 HARDENED STEEL IN DUAL JET MQL TURNING SYSTEM: A COMPARISON STUDY BETWEEN CVD AND PVD-COATED CARBIDE TOOLS
Authors: BAG RABINARAYAN , PANDA AMLANA , SAHOO ASHOK KUMAR , KUMAR RAMANUJ
Published: August 2024

The hard part turning process used to finish the cylindrical surfaces has some critical machining responses like tool wear with the lower quality of the machined surfaces. Minimum quantity lubrication (MQL)-based machining environment is currently considered an advanced cooling and lubrication technique. Cutting speed, feed rate, and cutting depth are considered input parameters, and the TaguchiL27 orthogonal array (OA) has been adopted as the design of the experiment. In this context, average surface roughness ([Formula: see text]), tool flank wear (VBc), cutting temperature ([Formula: see text]C), and chip morphology have been selected as measured outputs. To improve the machining performance of hardened steel, a sustainable MQL ([Formula: see text] directions) cooling system has been employed at the cutting zone. Iron–aluminum LRT 30 was considered as the base fluid for the hard turning of AISI 4340 steel by PVD (AlTiN) and CVD (TiN/TiCN/Al2O3) coating cutting inserts. The primary effects of process parameters and their influences on the measured outputs have been discussed. The dual jet MQL-based hard turning was performed at three levels of cutting speeds (80, 170, and 260[Formula: see text]m/min), feed rate (0.5, 0.1, and 0.15[Formula: see text]mm/rev), and cutting depth (0.2, 0.3, and 0.4[Formula: see text]mm) with an air pressure of 5[Formula: see text]bar and mist flow rate 50[Formula: see text]ml/h. The findings showed that PVD-coated carbide tools outperformed CVD-coated tools. The model of the measured outputs of both tools has been developed using second-order regression analysis. Utilizing ANOVA analysis, all the predictive models were observed to be significant and acceptable with larger than 90% of [Formula: see text] value.
METAL-SUPPORTED SOLID OXIDE FUEL CELLS: SYNTHESIS AND ELECTROCHEMICAL PROPERTIES ANALYSIS USING OPTIMIZATION METHOD
Authors: PATNAIK RAJESH KUMAR , DWIVEDI YAGYA DUTTA , SRIVASTAVA BIPIN KUMAR , ARULMOZHI A. , PRASAD D. V. S. S. S. V. , DILLIBABU SURRYA PRAKASH , GOPALA GUPTA AMARA S. A. L. G. , RAJARAM A.
Published: August 2024

Recently, metal-supported solid oxide fuel cells (MS–SOFCs) have been in the spotlight again for their design, thanks to their inexpensive materials, robustness, resistance to thermal cycling, and benefits of manufacturability. Hydrogen energy electrochemical devices, like MS–SOFCs, have a lot of potential. They are very ideal substitutes for solid oxide fuel cells (SOFCs) that utilize electrolytes or ceramic electrodes as their carrier basis, because of their greater durability, mechanical stability, heat cycle resistance, and rapid startup time. Even though MS–SOFCs have several advantages over conventional ceramic-based SOFCs, researchers are still struggling to perfect them due to issues such as selecting the appropriate metal-based material for the electrodes (anode, electrode) and comprehending how they deteriorate. This limitation might be evaded by optimizing the pore former filling and the diameter of the metallic supports (130–250[Formula: see text][Formula: see text]m). Optimization methods, such as particle swarm optimization, as well as penetration cycle numbers (1–15), as well as the impacts of fire temperatures (400–900∘C), were investigated to aid in optimizing the catalyst infiltration procedure. The enhanced cell outperformed its original performance by a factor of three, reaching an ideal energy density of 0.9[Formula: see text]W cm[Formula: see text] at 700∘C when powered by hydrogen. The improved cells had a 2% degradation rate per 100[Formula: see text]h at 550∘C, a 4.5% degradation rate at 600∘C, and a 5.5% degradation rate at 700∘C. We used electrochemical impedance spectroscopy and scanning electron microscopy to look at the catalyst’s mass shipping, coarsening, and chromium poisoning.
INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH
Authors: EZHILAN M. , KAILAINATHAN S. , SEIKH A. H. , ALAGARSAMY S. V.
Published: August 2024

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.
TIN-ANTIMONY OXIDE-GRAPHITE COMPOSITE AS ANODE MATERIAL WITH HIGH CAPACITY FOR LITHIUM-ION BATTERIES
Authors: TANG LIANGPENG , WEI RAN , XIE WENQING , LIU XIAOQING , SHANG CHEN , ZHANG JUNJIE
Published: August 2024

The severe capacity loss of tin-based anodes restricts their use in lithium batteries (LIBs). To enhance tin anode cycle stability, tin-antimony double-discharge anodes were designed. Additionally, by ball milling, tin-antimony oxide, and flake graphite cycling-stable composites were created. The stacked flake graphite buffers tin volume rise and enhances electrical conductivity, cyclic specific capacity, and rate performance. After 400 cycles, the tin-antimony oxide-graphite anode shows good cycling specific capacity (480[Formula: see text]mAh[Formula: see text]g[Formula: see text]). Ball milling double-discharge anode material and graphite will inspire novel carbon composite anode materials.
EXPERIMENTAL INVESTIGATION OF WEAR BEHAVIOR OF Al 8011/FLY ASH/B4C HYBRID METAL MATRIX COMPOSITE
Authors: SASIKUMAR K. S. K. , NANDAKUMAR N. S. , GUKENDRAN R. , SAMBATHKUMAR M.
Published: August 2024

Composite materials are combinations of distinct materials, engineered to create new materials with specific properties. Stir-casting method is used to prepare composites, it involves heating Aluminium Alloy (Al 8011) reinforced with B4C (Boron Carbide) and fly ash at 5, 10, and 15[Formula: see text]vol.%. The molten material is poured into a die to prepare the samples for testing. The composite’s density is measured by the principle of Archimedes, which decreases as the reinforcing particles increase, and then density and porosity are compared. The wear test specimen was prepared as per standard. The design model is generated by applying the RSM (Response Surface Methodology) equation with various parameters like load, sliding velocity of the pin, and track distance with values of 1–2[Formula: see text]kg, 1.5–2.5[Formula: see text]m/s, and 1000–3000[Formula: see text]m, respectively. The developed ANOVA (Analysis of Variance) result suggests that the created model is significant. The validation test was run and the results showed mean value of 0.0021356 and this value is in the 95% prediction range. The SEM (Scanning Electron Microscope) images of wear-tested composite show the delamination layers and deep groves. EDS (Energy Dispersive Spectroscopy) is used with electron microscopes to determine the chemical composition of wear out specimens. By the end of this study, it is discovered that the wear rate lowers with increasing reinforcement percentage while increasing with load and sliding velocity.
FABRICATION OF AA1100/Fl2O3 COMPOSITE STRIPS AND THE IMPROVEMENT OF THEIR ELECTROMAGNETIC, MECHANICAL AND FRACTURE PROPERTIES
Authors: RODRIGUES PAUL , SINGH SANDEEP , SINGH ASHISH , ABOSAODA MUNTHAR KADHIM , CASTAGNE X. P.
Published: August 2024

In this study, forming limit diagrams (FLDs), fracture toughness (FT), mechanical, magnetic, and electro-physical properties of Al/Fe2O3 composites have been investigated experimentally. Accumulative-roll-bonding (ARB) process was used to manufacture composite samples. Then, Al/Fe2O3 composites have been produced at 300∘C up to eight ARB passes. Also, magnetic composites have been fabricated with 0, 5% and 10[Formula: see text]wt.% of particles. The interlayer bonding quality was improved at higher passes. By studying the SEM rapture morphology, it was shown that the rapture mode changed to shear ductile for samples with more passes. For composite samples fabricated at higher passes and in comparison with the annealed sample, elongated dimples were turned to shallow dimples. FLDs area as the main forming criterion dropped severely after pass one and then began to improve at higher passes. Fracture test results showed that after the 8th pass, the value of FT improved gradually to the maximum value of 34.3[Formula: see text]MPam[Formula: see text].
GREEN SYNTHESIS OF CaO–PdNps FROM LAWSONIA INERMIS LEAF EXTRACT: CATALYTIC REDUCTION OF METHYLENE BLUE WITH CENTRAL COMPOSITE DESIGN-ENHANCED NEURAL NETWORK PREDICTION
Authors: BELDJILALI MOHAMMED , IKRAM DRAI , MEKHISSI KHALED , TAHA INAS , ISMAIL ISSAM , ECH-CHERGUI ABDELKADER NEBATTI , BELARBI LAHCEN , BOUSALEM SMAIN
Published: August 2024

In this work, small-sized CaO–PdNps nanocomposites were synthesized using Lawsonia inermis leaf extract as a reducing and capping agent. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were used to analyze the produced nanocomposites. Based on these investigations, the average particle size was found to be [Formula: see text][Formula: see text]nm, with a predominantly spherical morphology. The resulting nanocomposite was then tested for catalytic activity in methylene blue reduction. The mass of the catalyst and dye, the concentration of sodium borohydride ([NaBH4]), and the reaction time were all optimized using a central composite design (CCD). A significant correlation between predicted and experimental degradation percentages was shown by the CCD model ([Formula: see text]), which also proved statistically significant (p-[Formula: see text]). Furthermore, a deep learning neural network (DLNN) was used to improve the prediction accuracy over and beyond CCD. There was a good connection between the two models as a result of using the CCD output as DLNN validation data. Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and [Formula: see text] were used to assess the performance of the DLNN. The results ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) validated that all four metrics were successful in forecasting the percentage of degradation.
FABRICATION AND CHARACTERIZATION OF BISMUTH TELLURIDE THIN FILMS BY THERMAL EVAPORATION TECHNIQUE
Authors: VANI PRIYADHARSHINI S. , PANDI P. , NEYVASAGAM K. , MARIAPPAN A.
Published: August 2024

An interest in the study of the structural and optical properties of topological insulators, led to the selection of Bismuth telluride thin films. Among the topological insulators, Bismuth telluride is one of the promising materials that exhibit applications in the field of thermoelectric power generation. The films were prepared using the thermal evaporation method under vacuum conditions of [Formula: see text] mbar on glass substrates maintained at a temperature of 120∘C. The thickness of the film samples ranges from 400 Å to 1400 Å. Pure phases of Bi2Te3 with a rhombohedral structure are confirmed using X-ray Diffraction. The crystallite size was calculated using Debye–Scherrer’s formula. Structural morphology and compositions are examined using SEM and EDAX. The UV–Vis study reflects the optical behavior of the thin film samples. The absorption spectra gives the direct and allowed transition band gap of the Bi2Te3 thin film samples for various thicknesses. The band gap decreases with the increase in the thickness of the Bi2Te3 thin films, suggesting that the material has significant absorption towards the visible spectrum. The Photoluminescence (PL) emissions for Bi2Te3 thin film samples of various thicknesses are examined and analysed. Raman study reveals the presence of two active modes.