Comparison Result Research Articles

Ionospheric response and the impact on GPS positioning accuracy during the Tonga volcano eruption on 15 January 2022

The characteristics of ionospheric disturbances and GPS positioning accuracy during the eruption of the Hunga Tonga-Hunga Ha'apai (Abbreviated as 'Tonga' in this paper) on January 15, 2022 are investigated using International GNSS Service (IGS) and Swarm data. Firstly, the comparison of ionospheric responses between on January 15, 2022 and November 5, 2021 is conducted. The result shows that the number of the rate of TEC index (ROTI) values more than 0.5 TECU/min during Tonga Volcano eruption was significantly larger under the same moderate geomagnetic storms. Moreover, the values of Detrend Vertical Total Electron Content (DVTEC) above IGS stations were increased to 0.3–1 TECU on January 15, 2022. An interesting finding is shown that there was a linear corrleation between the DVTEC increment and the distance from the stations to Tonga Volcano. On the contrary, the DVTEC values on November 5, 2021 were all less than 0.1. Secondly, GPS positioning accuracy on January 15, 2022 and November 5, 2021 is analyzed, respectively. The GPS positioning accuracy was declined on January 15, 2022. Even, the GPS positioning errors of some stations were larger than 3 m. By comparision, the GPS positioning errors of the studied IGS stations on November 5, 2021 were all less than 0.2 m. The above comparison result indicates that the Tonga Volcano eruption on January 15, 2022 could affect the GPS positioning accuracy. At the same time, the GPS cycle slips of the studied IGS stations are obviously enhanced during the Tonga Volcano eruption. Finally, the interaction mechanism between the Tonga volcano eruption, ionospheric disturbances and GPS positioning accuracy is discussed and explored.

A semi‐Lagrangian ε$$ \varepsilon $$‐monotone Fourier method for continuous withdrawal GMWBs under jump‐diffusion with stochastic interest rate

AbstractWe develop an efficient pricing approach for guaranteed minimum withdrawal benefits (GMWBs) with continuous withdrawals under a realistic modeling setting with jump‐diffusions and stochastic interest rate. Utilizing an impulse stochastic control framework, we formulate the no‐arbitrage GMWB pricing problem as a time‐dependent Hamilton‐Jacobi‐Bellman (HJB) Quasi‐Variational Inequality (QVI) having three spatial dimensions with cross derivative terms. Through a novel numerical approach built upon a combination of a semi‐Lagrangian method and the Green's function of an associated linear partial integro‐differential equation, we develop an ‐monotone Fourier pricing method, where is a monotonicity tolerance. Together with a provable strong comparison result for the HJB‐QVI, we mathematically demonstrate convergence of the proposed scheme to the viscosity solution of the HJB‐QVI as . We present a comprehensive study of the impact of simultaneously considering jumps in the subaccount process and stochastic interest rate on the no‐arbitrage prices and fair insurance fees of GMWBs, as well as on the holder's optimal withdrawal behaviors.

Multi-Branch Dilation Convolution CenterNet for Object Detection of Underwater Vehicles

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods.

Bit leakage-resistant quantum private magnitude comparison

Lots of ready quantum private comparison (QPC) protocols usually only judge whether two users’ data are equal, whereas the quantum private magnitude comparison (QPMC) protocol can compare the magnitude of two numbers, whose results are greater than, equal and less than. It is by announcing a comparison result of two bits in two data that the existent QPMC protocols determine the size relationships of the whole data. Although the announcement leaks no information to outside attackers, yet it alerts two participants to know each other’s value of a certain bit. This is termed as bit leakage in this paper. In order to remedy this shortage, the paper proposes a novel QPMC protocol using a type of Bell state. The protocol is thoroughly analyzed, correct and secure. Also, it proves free of bit leakage. Additionally, because it is easier to implement because of the usage of a type of Bell state, it counts as a better alternative for QPMC.

On the symmetric rearrangement of the gradient of a Sobolev function

In this paper, we generalize a classical comparison result for solutions to Hamilton–Jacobi equations with Dirichlet boundary conditions, to solutions to Hamilton–Jacobi equations with non-zero boundary trace. As a consequence, we prove the isoperimetric inequality for the torsional rigidity (with Robin boundary conditions) and for other functionals involving such boundary conditions.

Master Bellman equation in the Wasserstein space: Uniqueness of viscosity solutions

We study the Bellman equation in the Wasserstein space arising in the study of mean field control problems, namely stochastic optimal control problems for McKean-Vlasov diffusion processes. Using the standard notion of viscosity solution à la Crandall-Lions extended to our Wasserstein setting, we prove a comparison result under general conditions on the drift and reward coefficients, which coupled with the dynamic programming principle, implies that the value function is the unique viscosity solution of the Master Bellman equation. This is the first uniqueness result in such a second-order context. The classical arguments used in the standard cases of equations in finite-dimensional spaces or in infinite-dimensional separable Hilbert spaces do not extend to the present framework, due to the awkward nature of the underlying Wasserstein space. The adopted strategy is based on finite-dimensional approximations of the value function obtained in terms of the related cooperative n n -player game, and on the construction of a smooth gauge-type function, built starting from a regularization of a sharp estimate of the Wasserstein metric; such a gauge-type function is used to generate maxima/minima through a suitable extension of the Borwein-Preiss generalization of Ekeland’s variational principle on the Wasserstein space.

Study on two wheeler helmet for reducing head injuries on different 3D-printed materials: A numerical analysis

A helmet is essential for bike riders to protect the head injuries. It has been designed in such a way that it can protect against injuries to the head, so it requires more concern to protect during an accident. Many accidents have been reported, and thousands of life has been lost due to head injuries. Head injuries have happened with the external load, which not gives the natural protection by the head skull. So, this study analyzed the helmet structure and identified the major area of damage to protect the human head. This study may help the helmet industries provide better safety in the major concern area of the helmet. Here, ANSYS software was used to analyze the helmet structure with different boundary conditions, which are fixed bottom and load applied on the top area, fixed bottom and applied load on the top line, left side fixed and applied load on the right side and right side fixed and applied load on the left side of the helmet. In addition, find an optimum material to manufacture the helmet structure with lightweight and better durability during an accident. Here, the material is considered as high impact polystyrene (HIP), thermoplastic elastomer (TPE), polypropylene (PP) for the helmet structure. The simulation has been taken care of for the static condition, such as total deformation and von Mises for the considered boundary conditions. Additionally, a dynamic analysis is to identify the total deformation of the helmet. The comparison result shows that the HIPS material was the most suitable and acceptable material per the BIS standard experimental data.

Perbandingan Hasil Belajar Sepak Bola Menggunakan Model Pembelajaran Kooperatif Tipe Jigsaw Pada Siswa Kelas XI SMK Negeri 8 Seram Bagian Barat

This is comparative research, The variable was the comparison result of learning for Passing in the Football Game using the TGT and Jigsaw Learning Models for 25 students in class XI SMK Negeri 8 West Seram, Data sources come from teachers, students and researcher. Data collection techniques using observation, tests, and documentation or archives. the good results, and Very Good criteria (89-100), the TGT learning model amount to 4 students and the TGT Jigsaw learning model amount to 1. Explained in Figure 4.3. For obtain good criteria (77-88) the TGT learning model, were 13 students and the Jigsaw learning model, were 14 students. The sufficient criteria (65-76) for the TGT learning model are 6 students and the Jigsaw learning model is 10 students. Meanwhile (<=64) is only found in TGT with 2 students.

A Double-Edge-Triggered Digital LDO with Built-In Adaptive VCO Clock for Fast Transient Response and Low Power Consumption

A double-edge-triggered digital low dropout regulator (DLDO) is proposed with a built-in adaptive voltage-controlled oscillator (VCO) clock (AVC) for a system-on-chip (SoC) application. To achieve a fast transient response, the main comparator generates the comparison result at the rising edge of the AVC, and this result is sampled by the coarse or fine bidirectional shifter register at the falling edge of the AVC. Furthermore, the clock frequency can be boosted from 8 MHz at the steady state to 50 MHz by the AVC when the output current suffers from a sudden change, and it can also be adjusted in real-time according to the output voltage, which avoids the oscillation phenomenon and decreases the power consumption during the recovery process. To further lower the power consumption, the self-clock comparator replaces the conventional static comparator in the transient detector. The post-simulation results show that the proposed DLDO consumes a quiescent current of 95.13 μA in the steady state, and drives a maximum load current of 25 mA at the supply power of 0.6 V with an active area of 0.053-mm2 in a 180 nm CMOS process. When the load current jumps from 0.5 mA to 25 mA at the edge of 100 ps, the undershoot voltage and overshoot voltage are only 335 mV with the recovery time of 2.7 μs and 47.6 mV with the recovery time of 2.1 μs at the total on-chip capacitor of 50 pF, respectively, resulting in two competitive figures of merits (FoMs) than the previous works.

Experimental investigation on distribution of boric acid in a vertical 1 × 2 rod array channel

During the reactor operation, the mixing mass flow of boron acid caused by diversion cross-flow, turbulent mixing and void drift changes the boron concentration distribution in the coolant and affects the amount of boron in the Chalk River Unidentified Deposit (CRUD). Accumulation of boron within the porous crud layer on the fuel rods in the cores of pressured water reactors (PWR) results in the crud-induced power shift (CIPS), threatening the operation safety of reactors. In order to obtain the mixing mass flow of boric acid between adjacent channels, a vertical test channel containing two subchannels simulating a PWR fuel rod bundle is constructed. Using this channel, flow distributions and mixing mass flow of boric acid are measured for single-phase and two-phase air–water flows. The measurements show that the boric acid distribution is affected by turbulent mixing for single-phase flow and the mixing mass flow of boric acid increases with the Reynolds number. For two-phase flow, the bubble-induced turbulence enhances the turbulence intensity of the liquid flow, the mixing mass flow of boric acid increases with the increase of void drift and reaches the maximum in the slug flow. The experimental data is used to support the development and validation of a new transverse source term model of boron mixing. The comparison result shows the significant improvement of the code prediction.

Sustainable Hydrodynamic of Artificial Neural Networks and Logistic Regression Model to Lane Change Serious Conflict at Unsignalized Intersection on Malaysia’s Federal Route

The neural network approach is used to resolve the interdependencies between inputs and outputs in a major conflict on the Malaysian Federal Route’s unsignalized intersection (UI). Two group of datasets being use in this study, they are right turning motor vehicle (RTMs) which consist eight hundred forty-five data and serious conflict lane change model, consist three hundred sixty-three datasets. The aim of the study is to develop serious conflict lane change model by using artificial neuron network (ANN) and logistic regression method (LRM) and to identify the risk of right turning motor vehicle (RTMs) behavior. The input data consists traffic behavior, type of gap, road geometry and traffic facilities. Subsequently the output data will be lane change serious conflict, the study utilize SPSS 26 statistic software. The first section of the study covers the neural network, while the second section describes the strategy utilized to analyses the scientific work. Third part cover the simple analysis of lane change serious conflict and analysis of gap types. Last part is comparison result between logistic regression method and artificial neuron network. This study reveals that lane change due angular conflict, speed limit 50kph, Gap 3 type, second passenger car, motorcycles, motorcycle stop near passenger car, traffic light and lane width were found significance in lane change serious conflict models. The study has determined that RTMs is motorcycle as vulnerable mode of transport. Besides, this study found that the combination between both method ANN and LRM sustainable hydrodynamic and complement each other especially on explaining the outcomes result.

A Comparison of Ocean Wave Height Forecasting Methods for Ocean Wave Energy Conversion Systems

Ocean wave height plays an important role in the operation status of ocean wave energy conversion systems. In this paper, the future continuous ocean wave height within 2~3 s is forecasted by three methods, the autoregressive moving average model (ARMA) method, backpropagation (BP) neural network method, and radial basis function (RBF) neural network method. Then, the error between suggested forecast results and corresponding measured results are compared by the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient R. The comparison result indicates that the RBF neural network method is preferred to the other two methods, having the advantage of high accuracy. Lastly, the reasons for the errors of the three forecasting methods are analyzed. This study signifies that the RBF neural network method is beneficial to the operation control and efficiency improvement of ocean wave energy conversion systems.

Multi-Party Sequential Data Publishing Under Differential Privacy

Given a set of local sequential datasets held by multiple parties, we study the problem of publishing a synthetic dataset that preserves approximate sequentiality information of the integrated dataset while satisfying differential privacy for each local dataset. The existing solutions for publishing differentially private sequential data in the centralized setting mostly adopt tree-based approaches. Such approaches rely on different tree structures that encode sequential data's statistical information. The construction of a tree structure is normally done by recursively splitting nodes whose noisy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scores</i> (e.g., entropy or count) are larger than a given threshold. However, extending similar ideas to the multi-party setting is challenging. First, the comparison between noisy scores and a given threshold needs to be done in a distributed manner without letting the parties know the noisy scores, while satisfying differential privacy for each local dataset. Second, in the multi-party setting the large number of node splitting decisions incurs prohibitive computation costs. In addressing the above challenges, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPST</i> , a distributed prediction suffix tree construction solution. In DPST, we first introduce a novel node splitting decision method that calculates the comparison result under encryption with substantially improved efficiency. Then we present a novel batch-based tree construction approach to reduce computation costs. In order to achieve high parallel performance without incurring any extra communication cost, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conjunction</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slide</i> methods to ensure that each batch contains a stable number of carefully arranged <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decision tasks</i> . To further reduce communication and computation costs, we propose a prefix-based pre-pruning method to reduce the number of nodes that need to be judged whether to split by an interactive protocol. Extensive experiments on real datasets demonstrate that our DPST solution offers desirable data utility with low computation and communication costs.

Convolutional Neural Network for Identification of Personal Protective Equipment Usage Compliance in Manufacturing Laboratory

Data from the Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan Indonesia from 2019 to 2021 shows that the number of work accident victims who claimed Work Accident Insurance (Jaminan Kecelakaan Kerja / JKK) continues to increase. The high number of work accidents is mostly caused by unsafe behavior at work sites, one of which is in terms of compliance with the use of Personal Protective Equipment (PPE). One tool that is considered important as a step in reducing work accidents is an identification system for compliance of personal protective safety equipment use that can detect PPE used by visitors or workers. This study develops an automatic identification system that is built using the Convolutional Neural Network (CNN) to identify the use of PPE in the manufacturing technology laboratory. The CNN models used are the 4th and 5th versions of You Only Look Once (YOLO) which are then compared based on two methods: train from scratch and transfer learning. The dataset used for building the detection system has 11,579 images consisting of six classes of PPE objects. Overall performance of the proposed models shows very good results. Moreover, the comparison result among the three models shows that YOLOv5 transfer learning has the best performance with the best precision (94.2 %), recall (91.8 %), and mAP (88.6%).

A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN

To solve the problem of large interference of shearer rocker arm vibration signal and difficulty in feature selection, and recognize accurately the cutting status of shearer, a novel pattern identification method based on Symmetrized Dot Pattern (SDP), Local Mean Decomposition (LMD) and Multi-Scale Convolution Kernel Deep Convolutional Neural Network (MCK-DCNN) is presented. Firstly, the vibration signal of shearer rocker arm is decomposed by LMD to get multiple product functions (PFs). The previous three PFs are transformed into SDP images with different features by SDP method, which are input into MCK-DCNN model to automatically extract features and identify shearer cutting status. The method can achieve the classification rate of 97.9%, which is superior to 1D_CNN and LeNet. The comparison result indicates that the method can provide technical support for improving the automatic coal cutting performance of the shearer.

Tree Species Classification Using Optimized Features Derived from Light Detection and Ranging Point Clouds Based on Fractal Geometry and Quantitative Structure Model

Tree species classification is a ubiquitous task in the forest inventory field. Only directly measured feature vectors have been applied to most existing methods that use LiDAR technology for tree species classification. As a result, it is difficult to obtain a satisfactory tree species classification performance. To solve this challenge, the authors of this paper developed two new kinds of feature vectors, including fractal geometry-based feature vectors and quantitative structural model (QSM)-based feature vectors. In terms of fractal geometry, both two fractal parameters were extracted as feature vectors for reflecting how tree architecture is distributed in three-dimensional space. In terms of QSM, the ratio of length change and the ratio of radius change of different branches were extracted as feature vectors. To reduce the feature vector dimensionality and explore valuable feature vectors, feature vector dimension reduction was conducted using the classification and regression tree (CART). Five hundred and sixty-eight individual trees with five tree species were selected for evaluating the performance of the developed feature vectors. The experimental results indicate that the tree species of Fagus sylvatica achieved the highest overall accuracy, which is 98.06%, while Quercus petraea obtained the lowest overall accuracy, which is 96.65%. Four other classical supervised learning methods were adopted for comparison. The comparison result indicates that the proposed method outperformed the other four supervised learning methods no matter which accuracy indicator was adopted. In comparison with the relevant method, the eight feature vectors developed in this paper also performed much better. This indicates that the fractal geometry-based feature vectors and QSM-based feature vectors developed in this paper can effectively improve the performance of tree species classification.

A digital literacy predictive model in the context of distance education

Digital technologies are essential in the distance education environment. Several learners who enrol in distance education programs have difficulty using the technologies during the learning sessions. This study focuses on factors influencing digital literacy in distance education programs. A predictive model was developed based on four phases of the study; preliminary study, dataset preparation, experimental design, and analysis. For this study, 232 survey data were successfully collected. The experimental design has two phases, including data pre-processing and feature selection. - The target class is defined based on the device used and the generation. Five machine learning algorithms have been selected for the classifier model in the data analysis phase. They are logistic regression as a baseline model, k‐nearest neighbour, random forest, support vector machine (SVM), and multilayer perceptron. In addition, five cross-validation settings were chosen, which are 2, 3, 5, 10, and 20‐fold cross‐validation. The comparison result for two‐target data will be based on accuracy, precision, recall, and F-measure. Using the SVM algorithm, the best model shows the maximum accuracy with 82.9% for model learning based on generation and 41.4% for model learning based on the device. This study identifies interesting patterns based on learners’ generation. It shows that different generations have various ways of using digital literacy. Thus, the proposed predictive model proves that generation is a useful indicator to recognize the level of digital literacy among higher education learners, especially those for distance learning.

Probabilistic-Assured Resource Provisioning With Customizable Hybrid Isolation for Vertical Industrial Slicing

With the increasing demand of network slices in vertical industries, slice resource provisioning in transport networks has encountered two challenges, one is efficient slice resource provisioning in the presence of traffic uncertainty of slices, and another is flexible slice resource isolation for customizable isolation needs. In this paper, we propose an innovative flexible hybrid isolation model to support any customized resource isolation from complete isolation to full sharing, and solve the slice resource provisioning problem named Hybrid Slicing Minimum Bandwidth (HSMB) by considering traffic prediction error to mitigate the negative impact of traffic uncertainty in the proposed model. After analyzing the HSMB problem, 1) we first try to solve the problem in steps and decompose the HSMB problem into grouping sub-problem and adjusting sub-problem, 2) we then propose a low-complexity dynamic programming grouping algorithm and a fast iterative adjustment algorithm for the two sub-problems based on probabilistic feature-based analysis, 3) we combine the algorithms of the two sub-problems and further propose a linking algorithm for the potential insufficient resource dilemma and high computational complexity dilemma to improve the efficiency of the solution. The numerical results show that the proposed flexible hybrid isolation model with different factors can facilitate flexible slice isolation with customized isolation demands, while the proposed algorithm can realize efficient slice resource provisioning with a probabilistic guarantee. The comparison result shows the proposed algorithms outperform the other benchmark algorithms.

A Study on How to Improve the Accountability of National Defense Financial Information for Government Sustainability

Accrual-based accounting enables external users to make informed decisions by reporting an accounting entity’s management performance to them. It also helps managers accurately record resource usage and increase efficiency. Despite the Republic of Korea’s expectations for the introduction of accrual-based accounting in national accounting from 2011, aimed at achieving efficient resource management comparable to private companies, the results of the audit by the Board of Audit and Inspection indicate that the settlement of accruals in national accounting is still insufficient. This paper examines whether the financial reports compiled are suitable for competition in decentralized organizations. We present the status of accrual-based accounting in the Ministry of National Defense and analyze the actual cost index to control the efficient use of defense resources and financial information. For our analysis, we compared and analyzed the costs of two divisions and seven brigades in the Ministry of National Defense using cash-based budget spending and the actual cost index based on accrual basis, using data from the defense accounting system in 2015 and 2016. We found that the comparison result between the nominal cost and actual cost index was different. In particular, the actual cost index reflects the degree of control of the decision-maker who manages the resource. Our results show that evaluating the efficiency of resource management units in the Ministry of National Defense using the actual cost index can improve the Ministry’s accountability and achieve the purpose of introducing double-entry bookkeeping.

Comparison results for solutions of Poisson equations with Robin boundary on complete Riemannian manifolds

In this paper, by using Schwarz rearrangement and isoperimetric inequalities, we prove comparison results for the solutions of Poisson equations on complete Riemannian manifolds with [Formula: see text], [Formula: see text] or [Formula: see text], which extends the results in [A. Alvino, C. Nitsch and C. Trombetti, A Talenti comparison result for solutions to elliptic problems with Robin boundary conditions, Comm. Pure Appl. Math. 76(3) (2023) 585–603]. Furthermore, as applications of our comparison results, we obtain the Saint-Venant inequality and Bossel–Daners inequality for Robin Laplacian.