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EK-Chess: Chess learning system Based on Top-Level Chess Expert Knowledge Graph

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第7期41卷 4174-4188页

作者： Zhang, Mingyu Jin, Qiao Dong, Qian Wang, Danli Xie, Jun Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Minnesota Dept Comp Sci & Engn Minneapolis MN USA Shanghai Univ Sport Sch Phys Educ Shanghai Peoples R China Capital Univ Phys Educ & Sports Beijing Peoples R China

Chess, as a form of intellectual sport, has garnered significant attention from researchers, driving continuous research into computer-assisted player training. However, contemporary teaching or training models frequently confine learners to passive observation of computer-generated results. Beginners may find it challenging to comprehend the cognitive processes underlying decision-making. To address this issue, this article proposes EK-Chess, a knowledge graph-based chess teaching system that encompasses a series of endgame teaching scenarios. This system assists chess beginners in learning the positional evolution in pawn endgames, helping users comprehend offensive and defensive strategies in endgames. User studies validate the effectiveness, and support of the system in endgame learning.

关键词： learning system chess beginner chess education expert knowledge knowledge graph user study

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learning system for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain

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IEEE ACCESS 2020年 8卷 8129-8144页

作者： Zhou, Kai Wei, Ruixuan Zhang, Qirui Xu, Zhuofan Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China Natl Def Univ Peoples Liberat Army Joint Operat Coll Shijiazhuang 050084 Hebei Peoples R China

Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules;the parallel universe mechanism can significantly improve the system & x2019;s learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI & x0026;MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents & x2019;actions. Although it cannot be deployed quickly, it has a continuous learnin

关键词： Autonomous air combat bio-inspired cognitive mechanism long short-term memory learning system unmanned aerial vehicles

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learning system Development A Review of Approaches to Rethink learning in Different Contexts 7th

Learning System Development A Review of Approaches to Rethin...

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7th International Workshop on learning Technology for Education Challenges (LTEC)

作者： Siemsen, Sabine Naaranoja, Marja BKK Akad Alten Feld 30 D-36199 Rotenburg Germany Vaasa Univ Appl Sci Wolffintie 30 Vaasa 65200 Finland

ISBN: (纸本)9783319955223;9783319955216

learning systems during last decades are about to transform from isolated and specific singular ego-systems [30] with closely defined and clearly measurable learning-inputs and - outputs into networked environments for guidance and innovation, to develop holistic competences and to create future as social development. Connecting interdisciplinary contexts, larger amounts of very heterogeneous learners from formerly independent cultural contexts and developments, and building on technology and digitalization, they become a specific cultural space and environment of their own [36]. Traditional learning-scenarios are called into question and new cultures of learning are formed. Also, boundaries between science and organizations weaken and dissolve. This paper pursues to use the potential of combining theories of learning and change, to analyze innovative learning-approaches, to separate the contrast of incremental (sustaining) versus disruptive innovations and to use examples from the contexts of academic, vocational and organizational learning-processes.

关键词： learning system Innovation learning technology learning theories Theories of change learning-scenarios Multi-stakeholder Interdisciplinary learning environments learning in networks learning networks

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Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2006年第5期19卷 479-485页

作者： Malkhandi, Souradip Indian Inst Sci Ctr Elect Design & Technol Bangalore 560012 Karnataka India

The objective of this work is to develop a state-of-charge (SOC) estimation system for the lead-acid battery, which is free from the time-dependent variation of the battery characteristics. In this system, the SOC is estimated by an improved Coulomb metric method, and the time-dependent variation is compensated by using a learning system. The learning system tunes the Coulomb metric method in such a way that the estimation process remains error free from the time-dependent variation. The proposed learning system uses the fuzzy logic, which is not used for estimation of SOC but perform as a component of learning system. The fuzzy logic is used as a soft computing device for a multi-variables function evolution. During learning process the system automatically generates a new fuzzy rule base, and replaces the old fuzzy rule base. Results of the simulations as well as the experiments on an 8-bit microcontroller are also included which indicate the effectiveness of the proposed method. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： learning system SOC estimation fuzzy logic-based learning system

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Enhanced defect detection capability using learning system for extreme ultraviolet lithography mask inspection tool with projection electron microscope optics

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JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS 2016年第2期15卷

作者： Hirano, Ryoichi Hatakeyama, Masahiro Terao, Kenji Watanabe, Hidehiro EUVL Infrastruct Dev Ctr Inc 16-1 Onogawa Tsukuba Ibaraki 3058569 Japan Ebara Corp 4-2-1 Honfujisawa Fujisawa Kanagawa 2518502 Japan

Extreme ultraviolet lithography (EUVL) patterned mask defect detection is a major issue that must be addressed to realize EUVL-based device fabrication. We have designed projection electron microscope (PEM) optics for integration into a mask inspection system, and the resulting PEM system performs well in half-pitch (hp) 16-nm-node EUVL patterned mask inspection applications. A learning system has been used in this PEM patterned mask inspection tool. The PEM identifies defects using the "defectivity" parameter that is derived from the acquired image characteristics. The learning system has been developed to reduce the labor and the costs associated with adjustment of the PEM's detection capabilities to cope with newly defined mask defects. The concepts behind this learning system and the parameter optimization flow are presented here. The learning system for the PEM is based on a library of registered defects. The learning system then optimizes the detection capability by reconciling previously registered defects with newly registered defects. Functional verification of the learning system is also described, and the system's detection capability is demonstrated by applying it to the inspection of hp 11-nm EUV masks. We can thus provide a user-friendly mask inspection system with reduced cost of ownership. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： mask defect inspection extreme ultraviolet lithography learning system projection electron microscopy

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learning system for Myoelectric Prosthetic Hand Control by Forearm Amputees 23

Learning System for Myoelectric Prosthetic Hand Control by F...

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23rd IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN)

作者： Kawasaki, Haruhisa Kayukawa, Masayasu Sakaeda, Hirofumi Mouri, Tetsuya Gifu Univ Fac Engn Gifu 5011193 Japan Gifu Red Cross Hosp Gifu 5028511 Japan

ISBN: (纸本)9781479967650

This paper presents a novel learning system for myoelectric prosthetic hand control by forearm amputees using estimations of continuous joint angles. Wavelengths calculated using surface electromyogram (sEMG) signals of forearm amputees are input into a neural network (NN);past inputs are also used to take finger dynamics into consideration when estimating the metacarpophalangeal joint angles of each finger and wrist joint angle of pronation/supination and palmar flexion/dorsiflexion. The learning system has a three-step learning dataset generation process: (1) continuous motion of a virtual prosthetics hand (VR-hand) and motion timing bar are displayed to a subject;(2) the subject contracts his/her muscles following the VR-hand motion;and (3) sEMG signals and joint angles of VR-hand are measured and saved as the learning dataset. This system does not need to measure actual joint angles. To demonstrate the effectiveness of this learning system, RMS error of joint angle estimations are presented in cases of a motion set with 8 patterns for a healthy subject, and a motion set with 4 patterns for a right forearm amputee.

关键词： prosthetic hand neural network learning system

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A practical use of learning system using user preference in ubiquitous computing environment

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MULTIMEDIA TOOLS AND APPLICATIONS 2013年第2期64卷 491-504页

作者： Jeong, Hwa-Young Hong, Bong-Hwa Kyung Hee Univ Humanitas Coll Seoul 130701 South Korea Kyunghee Cyber Univ Dept Informat Commun Seoul 130701 South Korea

U-learning, or ubiquitous learning, has become the state-of-the-art educational trend in information technology. In ubiquitous learning environment, students now have the ability to learn anytime and anywhere. In many computer-assisted learning systems (such as e-learning, m-learning and u-learning), students must follow the lesson plans and learning environments established by the teacher. To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as self-directed learning, adaptive learning and personalized learning, have been developed. These techniques have been researched extensively, but they still do not consistently reflect the specific needs of all students. In this article, a u-learning system is proposed that considers the learning level and preferences of each learner. The system provides information about the student's preferred learning section and difficulty level of learning contents and indicates the areas that may require additional study (based on the educational history of the student), thus allowing students to set up an optimized learning environment. A topic preference vector was applied to calculate the student's preferences, and the learning section and the difficulty level were used as each vector value in the system. To verify the effectiveness of the proposed system, an English-learning system was implemented using content from the reading comprehension section of the Test of English for International Communication (TOEIC). An experiment was conducted using a control group and a test group. The results demonstrated that the system proposed in this paper is useful for improving learning efficiency.

关键词： Ubiquitous Computing Environment learning system User preference U-learning system

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EEG-Based learning system for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning systemS 2013年第10期24卷 1689-1700页

作者： Lin, Chin-Teng Tsai, Shu-Fang Ko, Li-Wei Natl Chiao Tung Univ Inst Comp Sci & Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Brain Res Ctr Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Biol Sci & Technol Inst Bioinformat & Syst Biol Hsinchu 300 Taiwan

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of similar to 82%.

关键词： Driving cognition electroencephalography (EEG) learning system motion sickness online estimation

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An Air Combat Decision learning system Based on a Brain-Like Cognitive Mechanism

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COGNITIVE COMPUTATION 2020年第1期12卷 128-139页

作者： Zhou, Kai Wei, Ruixuan Xu, Zhuofan Zhang, Qirui Lu, Hongxuan Zhang, Guofeng Air Force Engn Univ 1 Baling Rd Xian 710038 Peoples R China

The unmanned aerial vehicle (UAV) has emerged unexpectedly as a new force in the recent local wars. To occupy the high ground of military technology in the future, research on autonomous air combat using UAVs is extremely important. In this paper, we propose an intelligent air combat learning system inspired by the cognitive mechanism of the human brain. The air combat capability was divided into two parts: declarative and procedural memory. Imitating the updating and storage mechanism of human knowledge, a long- and short-term hierarchical asynchronous learning principle was designed. We adopted the basic idea of using the error signal to drive learning according to neurophysiological research. Drawing lessons from the working memory mechanism, the error signal for driving the short-term learning was created in the absence of labelled data, using only the interactive information. Then, we proved that the learning mechanism could ensure that system performance advances steadily and gradually. The experiments illustrated that the learning system designed in this paper can achieve some inferior confrontation ability through self-learning without human prior knowledge. Action strategies formed by learning are analogous to the classical tactical manoeuvres of human fighter pilots. We compared our work with related works and found that our method could improve its performance continuously and finally defeat its opponent.

关键词： Autonomous air combat learning system Brain-like cognitive mechanism Knowledge Multi-level memory Biological neural mechanism

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A Web-Based learning system for Software Test Professionals

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IEEE TRANSACTIONS ON EDUCATION 2011年第2期54卷 263-272页

作者： Wang, Minhong Jia, Haiyang Sugumaran, Vijayan Ran, Weijia Liao, Jian Univ Hong Kong Fac Educ Hong Kong Hong Kong Peoples R China Jilin Univ Changchun 130012 Peoples R China Oakland Univ Rochester MI 48309 USA South West Univ Chongqing 400715 Peoples R China

Fierce competition, globalization, and technology innovation have forced software companies to search for new ways to improve competitive advantage. Web-based learning is increasingly being used by software companies as an emergent approach for enhancing the skills of knowledge workers. However, the current practice of Web-based learning is perceived as being less goal-effective due to a lack of alignment of learning with work performance. To solve this problem, a performance-oriented approach is presented in this study. Using this approach, a Web-based learning system has been developed for software testing professionals. An empirical study was conducted by inviting employees working in the software testing sector to use and evaluate the system. The results showed the effectiveness of the proposed approach.

关键词： Education technology learning system software testing training

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