Xianchang Wang

ORCID: 0000-0001-8775-8188
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Research Areas
  • Logic, Reasoning, and Knowledge
  • Semantic Web and Ontologies
  • Rough Sets and Fuzzy Logic
  • Ginseng Biological Effects and Applications
  • Metaheuristic Optimization Algorithms Research
  • Smart Agriculture and AI
  • Fuzzy Logic and Control Systems
  • Bayesian Modeling and Causal Inference
  • Proteins in Food Systems
  • Advanced Algebra and Logic
  • Face and Expression Recognition
  • Logic, programming, and type systems
  • Evolutionary Algorithms and Applications
  • Advanced Neural Network Applications
  • Reinforcement Learning in Robotics
  • Robotic Path Planning Algorithms
  • Data Mining Algorithms and Applications
  • Advanced Image and Video Retrieval Techniques
  • Food Chemistry and Fat Analysis
  • Multi-Agent Systems and Negotiation
  • Advanced Algorithms and Applications
  • AI-based Problem Solving and Planning
  • Energy Load and Power Forecasting
  • Hydrocarbon exploration and reservoir analysis
  • Surfactants and Colloidal Systems

Shandong Academy of Agricultural Sciences
2012-2025

Jilin Province Science and Technology Department
2020-2024

Jilin University
2019-2024

Shenyang Aerospace University
2024

Ministry of Education of the People's Republic of China
2019-2023

Beijing Academy of Artificial Intelligence
2023

Ministry of Agriculture and Rural Affairs
2022

Dalian Ocean University
2008-2019

Jilin Medical University
2019

Dalian University of Technology
2006-2014

10.1016/j.patcog.2014.08.001 article EN Pattern Recognition 2014-08-13

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, article proposes a semisupervised oriented framework (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...

10.1109/tii.2024.3403260 article EN IEEE Transactions on Industrial Informatics 2024-05-31

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; also good feed for development animal husbandry essential raw material light industry, chemical medicine, health. Diseases are main factor limiting high stable yield maize. Scientific practical identification vital link to reduce damage diseases accurate segmentation disease spots fundamental techniques identification. However, single method cannot achieve effect meet diversity complexity spots....

10.3389/fpls.2021.789911 article EN cc-by Frontiers in Plant Science 2021-12-13

Chengcheng Chen1*Muyao Bai1Tairan Wang1Weijia Zhang1Helong Yu2*Tiantian Pang3Jiehong Wu1Zhaokui Li1Xianchang Wang1,3,4

10.3389/fpls.2024.1341335 article EN cc-by Frontiers in Plant Science 2024-02-21

10.1007/s12652-020-01969-1 article EN Journal of Ambient Intelligence and Humanized Computing 2020-05-06

In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems engineering and data science. When it comes to the particle swarm optimization (PSO), comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces strategy (CLS), which effectively boosts efficacy of PSO. However, when single modal function processed, convergence speed too slow converge quickly optimum during optimization. this paper,...

10.1155/2020/4968063 article EN cc-by Complexity 2020-10-19

In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single model, including attention mechanism, has varying effects on each subsequence. To accurately capture regularities using an we propose integrated model for series based signal decomposition two mechanisms. This combines results three networks—LSTM, LSTM-self-attention, LSTM-temporal attention—all trained obtained EMD....

10.3390/info14110610 article EN cc-by Information 2023-11-11

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability capture variations optical properties in seeds. As the data depends on actual germination rate, it inevitably results an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy collinearity inclusion hundreds wavelengths. It also creates challenge extract effective wavelength information selection, however,...

10.3389/fpls.2023.1322391 article EN cc-by Frontiers in Plant Science 2023-12-15

10.1016/j.asoc.2014.09.037 article EN Applied Soft Computing 2014-10-03

Summary The surface chemical composition and microstructure of walnut protein obtained through aqueous buffer bis(2‐ethylhexyl) sodium sulfosuccinate ( AOT ) reverse micelles were determined by X ‐ray photoelectron spectroscopy XPS scanning electron microscopy SEM ). surfaces characterised to monitor composition. different components C 1s, N 1s O peaks provided precisely. By comparison with from buffer, analysis revealed that the atomic percentage powder was higher, but percentages lower....

10.1111/ijfs.12345 article EN International Journal of Food Science & Technology 2013-09-13

Article Free Access Share on New HELIC-II: a software tool for legal reasoning Authors: Katsumi Nitta Institute Generation Computer Technology, 4-28, Mita 1-chome, Minato-ku, Tokyo 108, Japan JapanView Profile , Masato Shibasaki Tsuyoshi Sakata Takahiro Yamaji Wang Xianchang Hiroshi Ohsaki Satoshi Tojo Iwao Kokubo T. Anu Suzuki Authors Info & Claims ICAIL '95: Proceedings of the 5th international conference Artificial intelligence and lawMay 1995 Pages...

10.1145/222092.222260 article EN 1995-01-01
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