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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qu, Jing Qu, Ying Bai, Xueliang Xu, Lanxin Wang, Han Liu, Guixia Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China North China University of Science and Technology College of Science Tangshan China Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer's disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression. © 2024 IEEE.

关键词： Neuroimaging

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Optimizing YOLOv8-IC network for industrial scene recognition under camera surveillance

Optimizing YOLOv8-IC network for industrial scene recognitio...

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2024 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhang, Jiaming Chen, Jingtian Zhao, Peng Li, Yingxuan Yu, Biao Zhou, You JILIN Institute of Electronic Information Products Inspection Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

ISBN: (纸本)9781510689275

In the context of industrial automation and intelligent monitoring, accurate identification of the industrial environment is crucial to risk management. However, traditional scene classification methods struggle to capture the features of diverse structures and irregular shapes in complex industrial scenes. Additionally, they have shortcomings in extracting details and defining boundaries under dynamic lighting conditions. Deep learning-based methods, however, are better equipped to adapt to the complex backgrounds and dynamic detail variations in industrial environments through automatic feature learning. Although the YOLOv8 network benefits from the advantages of deep learning models, it still has limitations in meeting the recognition needs of complex industrial scenes. These limitations include difficulty in effectively capturing irregular structures and details, low positioning accuracy for overlapping boundaries, and susceptibility to interference from complex backgrounds. To address these issues, this paper introduces YOLOv8-IC (Industrial Classification), an improved version of YOLOv8, to meet the specific requirements of industrial scene classification. This enhancement improves feature extraction, target positioning, and key area focusing capabilities in complex environments. To further validate the model's effectiveness, a new dataset, Places24, was constructed by combining industrial scenes from the Places365 dataset with self-collected camera monitoring images. This approach enhances the model's applicability to real industrial environments. Experimental results show that the YOLOv8-IC network outperforms the original model in classification accuracy and real-time performance, significantly enhancing the stability and accuracy of industrial scene classification. The proposed YOLOv8-IC-based industrial scene classification scheme offers a more accurate solution for intelligent industrial monitoring systems. © 2025 SPIE.

关键词： Risk management

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A Simple Method to Synthesize ZIF-9 Metal-Organic Framework-Derived Co3o4 as Electrode Material for Supercapacitor 2

A Simple Method to Synthesize ZIF-9 Metal-Organic Framework-...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Zhou, Shuaitong Chen, Hui Zhang, Qi Liu, Gang Wang, Minzhen Changchun Institute of Architecture School of Electrical Information Jilin Province Changchun City China School of Electrical and Information Engineering Changchun Institute of Technology Changchun City China Control center State grid jilin Province Electric Power Co. LTD. Baicheng power supply company Baicheng City China School of Information and Engineering College of Science & Technology Ningbo University Ningbo City China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373318

In this paper, a unique structure with an outer porous block and an inner layered 'house-like' structure was prepared through the low-temperature thermal decomposition of bulk ZIF-9. This structure effectively prevents accumulation and greatly increases the specific surface area. The porous structure also ensures full contact between the electrode material and the electrolyte, thereby accelerating the ion transfer rate. This study investigated the morphology and properties of the materials formed at different calcination temperatures. At a current density of 1A/g, the electrode synthesized at 300°C exhibited the highest specific capacity of 438Fig. The prepared symmetric supercapacitor demonstrated a capacitance retention rate of 81.6% after 3500 cycles at a current density of 1A/g. Additionally, devices connected in series were able to light up a red LED for 3 minutes. The results indicate that the ZIF-derived material prepared by this method is a highly attractive candidate for supercapacitor applications and provides a new scheme for the synthesis of MOF-derived electrode materials. © 2024 IEEE.

关键词： Electrolytes

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Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

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Journal of Ocean University of China 2022年第2期21卷 323-330页

作者： WANG Xinhua ZHU Yungang LI Dayu ZHANG Guang School of Computer Science Northeast Electric Power UniversityJilin 132012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China State Key Laboratory of Applied Optics Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great challenges in un-derwater image and video *** improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this ***,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each *** methods aim to avoid undetection and misdetection of some pixels in image ***,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search *** results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.

关键词： ant colony optimization reinforcement learning underwater target edge detection

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series 41

An Empirical Examination of Balancing Strategy for Counterfa...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)

关键词： Time series analysis

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LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

LLDSNet: Limited labeled data segmentation network for COVID...

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2022 International Conference on Electronic Information engineering and computer science, EIECS 2022

作者： Liu, Jingyao Qu, Feng Shi, Weili Yang, Chen Jiang, Zhengang School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Computer and Information Engineering Chuzhou University Chuzhou China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China Bone and Joint Surgery The First Hospital of Jilin University Changchun China

ISBN: (纸本)9781510663312

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

关键词： COVID-19

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3D Printed Cylindrical Luneburg Lens

3D Printed Cylindrical Luneburg Lens

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Okramcha, Maxon Zuobin, Wang Fan, Yida Xu, Keren Arya, Ravi Kumar Dong, Junwei School of Physics Changchun University of Science and Technology Changchun130022 China Zhongshan Institute of Changchun University of Science and Technology Zhongshan528400 China Zhongshan Institute of Changchun University of Science and Technology Guangdong Zhongshan528400 China School of Optoelectronics Engineering Changchun University of Science and Technology Jilin Changchun China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Xiangshan Laboratory Zhongshan Institute of Changchun University of Science and Technology Zhongshan528400 China Xiangshan Laboratory Zhongshan Institute of Changchun University of Science and Technology Guangdong Zhongshan528400 China

ISBN: (纸本)9798350379693

Two-dimensional (2D) Luneburg lenses are attracting increasing research interest due to their unique beam collimation and steering capabilities. This paper explores the design and potential applications of a 2D Luneburg lens using PLA (polylactic acid) as the 3D printing material. Unlike the conventional Luneburg lens with different dielectric layers, we propose a design that utilizes only one material, and the refractive indices are varied by introducing air voids inside the substrate without compromising the structural or mechanical integrity of the lens. When fed with a waveguide, the proposed lens works in the X-band with a combined gain of approximately 12.7 dBi when fed by a waveguide. The lens was fabricated and verified by measurements. © 2024 IEEE.

关键词： Lens antennas

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Verification of the Method for Measuring the Thermal Conductivity of Solids with Dual-Wavelength Infrared Lasers - Taking 304 Stainless Steel as An Example

Verification of the Method for Measuring the Thermal Conduct...

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2022 Applied Optics and Photonics China: Infrared Devices and Infrared Technology;and Terahertz Technology and Applications, AOPC 2022

作者： Gu, Q.T. Zhai, H.X. An, B.L. Dong, W. Wang, X.Y. Men, Y.T. Zhao, Y.L. Wang, R.X. Beijing Engineering Research Center of Sustainable Energy and Buildings Beijing University of Civil Engineering and Architecture Beijing100044 China National Institute of Metrology Beijing100029 China Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9781510662247

In this paper, the dual laser induced measurement of thermal conductivity of solids is studied based on the laser-induced technique. Perfected the principle of dual-wavelength infrared laser-induced measurement of thermal conductivity of solids, and built an experimental system. According to the principle of measuring the thermal conductivity of solid with dual-wavelength infrared laser, an experimental system was built. Taking the fully oxidized 304 stainless steel as an example, the feasibility of the method and the stability of the system are verified;the relationship between thermal conductivity and temperature of 304 stainless steel is obtained;and the influence of the temperature inconsistency between the reference sample and the sample to be tested on the measurement results of the correction factor is analyzed. The results show that the experimental system for measuring the thermal conductivity of solids has good stability, and the stability is within 2% in the range of 873k ~ 1173k;the relationship between thermal conductivity and temperature of 304 stainless steel is K304=0.0144T+14.455;when the temperature is 973K and 1073K, the temperature deviation of the two samples is within 1K, the effect of temperature inconsistency is within 1.3%. © 2023 SPIE.

关键词： Austenitic stainless steel

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Implementation of MapReduce parallel computing framework based on multi-data fusion sensors and GPU cluster

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Eurasip Journal on Advances in Signal Processing 2021年第1期2021卷 1-14页

作者： Chang, Dajun Li, Li Chang, Ying Qiao, Zhangquan College of Computer Science and Technology Changchun University of Science and Technology Changchun 130022 Jilin China School of Electrical Information Changchun University of Architecture and Civil Engineering Changchun 130604 Jilin China School of Computer Science and Engineering Jilin University of Architecture and Technology Changchun 130114 Jilin China

Nowadays, with the rapid growth of data volume, massive data has become one of the factors that plague the development of enterprises. How to effectively process data and reduce the concurrency pressure of data access has become the driving force for the continuous development of big data solutions. This article mainly studies the MapReduce parallel computing framework based on multiple data fusion sensors and GPU clusters. This experimental environment uses a Hadoop fully distributed cluster environment, and the entire programming of the single-source shortest path algorithm based on MapReduce is implemented in Java language. 8 ordinary physical machines are used to build a fully distributed cluster, and the configuration environment of each node is basically the same. The MapReduce framework divides the request job into several mapping tasks and assigns them to different computing nodes. After the mapping process, a certain intermediate file that is consistent with the final file format is generated. At this time, the system will generate several reduction tasks and distribute these files to different cluster nodes for execution. This experiment will verify the changes in the running time of the PSON algorithm when the size of the test data set gradually increases while keeping the hardware level and software configuration of the Hadoop platform unchanged. When the number of computing nodes increases from 2 to 4, the running time is significantly reduced. When the number of computing nodes continues to increase, the reduction in running time will become less and less significant. The results show that NESTOR can complete the basic workflow of MapReduce, and simplifies the process of user development of GPU positive tree order, which has a significant speedup for applications with large amounts of calculations. © 2021, The Author(s).

关键词： Data scheduling GPU cluster Load balancing MapReduce parallel computing Multi-data fusion sensor

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Efficient MMSE Equalization for Direct-Sequence Spread-Spectrum Underwater Communications

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IEEE Internet of Things Journal 2025年第11期12卷 16325-16335页

作者： Liu, Mengzhuo Liu, Jun Peng, Zheng Cui, Jun-Hong Jilin University College of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Shenzhen Institute for Advanced Study UESTC Shenzhen518110 China Smart Ocean Technology Company Ltd. Shenzhen518057 China

To facilitate the exploration and exploitation of underwater resources, autonomous systems and underwater acoustic networks (UANs) are deployed for tasks unsuitable for direct human intervention and exchange information between devices. To keep the reliability of information exchanged, direct-sequence spread spectrum (DSSS) communication is commonly adopted for this scenario. To simplify the channel equalization process in DSSS communication, a minimum-mean-square-error (MMSE) equalizer is often utilized. However, the characteristics of underwater acoustic channels and acoustic modem, including long delay spreads and limited computational resources, lead to high computational complexity for MMSE equalization, thereby reducing decoding efficiency. To address this challenge, we propose a refined MMSE equalizer, termed the efficient MMSE equalizer (EME). Unlike conventional MMSE methods, the EME approach involves initially despreading the received chip sequence, followed by equalizing on the noisy symbols. By reducing the size of the correlation matrix in the core computational step of MMSE equalization, our method significantly improves computational efficiency. We assess the computational complexity of the proposed EME approach in comparison to conventional MMSE equalization and validate its performance through simulations and experimental studies. The results demonstrate that the EME achieves a bit error rate (BER) performance comparable to that of conventional MMSE equalization under high signal-to-noise ratio (SNR) conditions, while significantly enhancing computational efficiency. © 2014 IEEE.

关键词： Equalizers

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