Quantum properties of nitrogen-vacancy center in diamond coupled to mechanical resonatorsLiao, Qinghong; Xiao, Min; Qiu, Haiyan; Song, Menglin
doi: 10.1088/1742-6596/2595/1/012003pmid: N/A
The optical absorption spectrum of the hybrid system composed of the nitrogen-vacancy (NV) center coupled to the mechanical resonators is. investigated. Based on the pump-probe technology, the electronically induced transparency (EIT) is observed and the reasonable explanation for this transparency is given. We demonstrate that the position of the transparency is equal to the detuning difference. In addition, a completely new scheme to measure the coupling between the NV center and current carrying carbon nanotube mechanical resonator is obtained. Furthermore, an optical approach to measure the frequency of the vibrating graphene mechanical resonator is proposed. This work can provide some help for the application in the field of high-precision measurement and quantum information processing.
Effective machine learning-based skin disease diagnosis using PyTorchKumar, Rohit; Wang, Hwang-Cheng; Mukundan, B.; Gupta, Saurav Kumar; Kumari, C. Shyamala
doi: 10.1088/1742-6596/2595/1/012008pmid: N/A
The application of machine learning in medical diagnosis has become a trend in research. Skin infection is one of the most seen diseases and one of the world’s most infectious diseases, influencing people of all ages. The reason for the explicit attention of the researchers in skin detection is due to the reason that skin disease is more visible compared to any other disease. In the past, varied methods have been proposed, which have rendered remarkable results. However, the presently functional models are trained on specific kinds of diseases and are limited to 4 to 5 classes, which is inefficient in detecting a large set of diseases. The paper offers a weightless model for detecting 23 different kinds of skin diseases. The model is trained on the PyTorch backend, which gives the flexibility of developing an algorithm. The model attained 96.37% accuracy on training data, and 87.75% accuracy on test data, which is expected to improve as the size of the dataset is increased.
Peer Review Statementdoi: 10.1088/1742-6596/2595/1/011002pmid: N/A
All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.Type of peer review: Double anonymousConference submission management system: MorressierNumber of submissions received: 47Number of submissions sent for review: 30Number of submissions accepted: 15Acceptance Rate (Submissions Accepted / Submissions Received × 100): 31.9 %Average number of reviews per paper: 4.40Total number of reviewers involved: 132Contact person for queries:Name: Ms. Yan YANAffiliation: CTIS 2023Email: [email protected]
A depth discrimination method based on the scintillation of frequency domainWang, Xueyan; Gao, Yuan
doi: 10.1088/1742-6596/2595/1/012006pmid: N/A
An innovative algorithm of target depth discrimination suitable for passive horizontal array in shallow sea is discussed in this paper. Based on the idea of modal scintillation theory, a new target depth discrimination method of high calculation speed for horizontal array is proposed by analysing the commonness between frequency domain energy of low-frequency broadband signals and modal excitation. This method avoids the mode decomposition of the horizontal array signals, greatly reduces the complexity, improves the stability, has a fast calculation speed and can perform the real-time depth judgment. In this paper, the principle of the algorithm is derived in detail in the theoretical aspect, and then the algorithm is simulated in the shallow sea environment to verify its effectiveness, and the influence of different influencing factors on the performance of the algorithm is explored. At the same time, the algorithm is verified by the actual experimental data and achieves good results, indicating that the algorithm has certain practical application value in the target depth discrimination domain.
Research on the simulation environment of hard X-ray nanoprobe beamline stationLiu, Yuhao; Li, Aiguo; He, Yan; Zheng, Lifang; Zhao, Ying
doi: 10.1088/1742-6596/2595/1/012011pmid: N/A
In the hard X-ray nanoprobe beamline station, experiments need to be performed by adjusting the optical equipment in order to obtain good beamline performance. Due to many factors, only through the use of intelligent optimization, the beamline performance can be quickly improved. Therefore, an intelligent optimization method is proposed in this work to improve the performance of the beamline rapidly by using an adaptive algorithm to optimize the motor shaft of each device. Moreover, this paper introduces the simulation environment of the hard X-ray nanoprobe beamline equipment. In this environment, a multi-axis parallel optimization model of several optical devices is designed, and the ionization chamber feedback is replaced by Rastrigin function. Furthermore, the differential evolution algorithm is used to verify the model. The optimization tests of multiple devices in the beamline are carried out, and the automatic optimization of the devices is realized. As for the theoretical result, the designed intelligent beamline optimization program is capable of converging to the optimal value within 3-6 minutes on the simulation platform, this automated process could potentially enhance beamline adjusting efficiency by 10-20 times compared to manual beamline adjusting.
Position estimation of fuel ball blockage in pipelineZhou, Yihang; Xiao, Yufeng; Xie, Yuehui
doi: 10.1088/1742-6596/2595/1/012013pmid: N/A
When the high-temperature gas-cooled reactor is working, lots of fuel balls are transfered in the core pipelines. To enhance the operation efficiency, this paper proposes a simple and feasible estimation method for pipeline blockage detection. This method steps include as follows: Firstly, the three-dimensional models of possible blockage pipelines are formulated; once the blockage breaks out, the radiation counting rates are collected along a predefined route; subsequently, the estimation space is set from above models, and the particle filter algorithm is executed to complete the processes of weight updating, normalization and resampling; finally, the blockage position is calculated from the convergent particle set. Simulation experiments showed that this method could effectively work and accurately estimate the blockage location.
Prefacedoi: 10.1088/1742-6596/2595/1/011001pmid: N/A
This volume of Journal of Physics: Conference Series presents the proceedings of the 1st International Conference on Computer Technology and Information Science (CTIS 2023). The conference was held online during June 17-18, welcoming around 40 participants from home and abroad.CTIS 2023 organized discussions on hot topics Quantum Information, Signal Processing, and Computer Security and focused on the challenges during research as well as application. The conference showcases various sessions such as keynote speeches, oral reports, poster presentations, Q&A, etc. We had the utmost pleasure of having with us experts and scholars from around the globe sharing their latest findings and insights.The CTIS conference is conceived in the belief of promoting academic exchange within and across disciplines, addressing theoretical and practical challenges and advancing current understanding and application, during the process of which amity is spread, connections established and future collaborations enabled.The CTIS organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the keynote speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to future cooperation with them at future CTIS conferences to come.List of Honorary Chair, Conference Chair, Conference Co-chair, Technical Program Committee are available in this pdf.
Comparative evaluation for alternative variable importance rankings for pedestrian injury severitiesCheng, Yichi; Zhang, Yongping
doi: 10.1088/1742-6596/2595/1/012015pmid: N/A
Little research is dedicated to evaluating the performance difference of various metrics in ranking predictor importance in the traffic safety field. To this end, the main objective of the current paper is to evaluate and quantify different methods for sorting the variable importance related to crash severity. A comprehensive database for pedestrian-related crashes in the state of California was developed. Four popular measurement metrics used in the past were chosen for evaluation purpose: Mean Decrease Accuracy (MDA), Mean Decrease Gini (MDG), log-likelihood ratio test associated with multinomial logit model, and Principal Component Analysis (PCA). The former two metrics come under the same umbrella of the Random Forest (RF) technique, while the latter two are methods belonging to different domains. The results show the alternative methods yield different variable importance rankings with PCA being isolated from others. The two methods under the same domain of the random forest, or MDG and MDA, have the most common results, but still reveal a 17% ranking difference. It is anticipated that the results could raise more awareness of the importance of selecting the appropriate metrics to evaluate the predictor importance from different perspectives.
Quantum approximate optimization algorithm parameter prediction using a convolutional neural networkXie, Ningyi; Lee, Xinwei; Cai, Dongsheng; Saito, Yoshiyuki; Asai, Nobuyoshi
doi: 10.1088/1742-6596/2595/1/012001pmid: N/A
The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth p + 1 QAOA instance by the parameters from the depth p QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erdős–Rényi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.
Optimization of ICANet lightweight human pose estimation based on HRNetHou, Qing; Li, Kun; Xu, Zhen
doi: 10.1088/1742-6596/2595/1/012005pmid: N/A
The existing high-resolution human pose estimation models have low applicability in practical applications due to their large parameter quantity and high computational complexity. To address these issues, this paper proposes a human pose estimation network with a lightweight inverted residual coordinate attention network (ICANet) based on the high-resolution network (HRNet). With the introduction of CoordAttention mechanism and the inverted residual module, this paper proposes two lightweight network modules, namely ICAneck and ICAblock, which not only reduce model parameter quantity and computational complexity, but also achieve feature enhancement of long-range dependence and precise position information in spatial directions of the feature map. Experimental results show that compared to HRNet, the ICANet model proposed in this paper reduces its parameter quantity by 53.7% and computational complexity by 32.4% on the COCO validation set, and lowers its parameter amount by 53.7% and computational complexity by 32.6% on the MPII validation set. Practical applications prove that ICANet still achieves high-precision detection of human key points with fewer parameters and lower computational complexity, and has higher applicability and practicality compared with common human pose estimation networks such as the Stacked Hourglass Network (Hourglass), Cascaded Pyramid Network (CPN), and SimpleBaseline, and therefore has better applicability and practicality.