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Extending the behavioural paradigm for intelligent systems

Extending the behavioural paradigm for intelligent systems

引用

the 27th Hawaii International conference on System Sciences, HICSS-27

作者： Lindley, Craig A. Macquarie Univ New South Wales

ISBN: (纸本)0818650702

Based upon fundamentally different ideas about the nature of knowledge and intelligence, the behavioural approach to artificial intelligence is an alternative to traditional representational AI paradigms that overcomes specific limitations of traditional approaches. Behavioural control systems for autonomous mobile robots have demonstrated robust and effective performance. However, a comprehensive methodology for the behavioural paradigm has not yet been fully developed. Concentrating upon a behavioural methodology called the subsumption architecture, this paper describes methodological enhancements to the behavioural paradigm based upon the use of logic. the logical definition of behaviours allows many techniques from traditional artificial intelligence to be applied to the analysis, definition, and verification of behavioural systems, and supports strategies for achieving robustness, without incurring the problems associated with traditional AI.

关键词： Learning systems

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Reexamining Indoor Mobile robots from a Cognitive Perspective 5

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

引用

5th International conference on Robotics and computer vision, ICRCV 2023

作者： Song, Fei Li, Jinyu Tang, Fengzhen Tang, Yandong Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

ISBN: (纸本)9798350326369

the field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. this article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. these phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques. © 2023 IEEE.

关键词： Navigation

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Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional computer vision

Exploring the Trade-Off Between Performance and Cost in Faci...

引用

intelligent Systems conference (IntelliSys)

作者： Chen, Jan-Jo Husnain, Ali Cheng, Wei-Wei Chicago State Univ Chicago IL 60628 USA Northrop Grumman Chicago IL 60606 USA

ISBN: (纸本)9783031477232;9783031477249

During the late 20th century, the rise of computers and technology led to collaborations between mathematicians and computer scientists to develop systems that could solve various problems using traditional approaches. One such problem was facial recognition, which enables the identification of human faces without interference. Traditional machine learning methods were used to build these systems until recently, when deep neural networks trained with massive datasets replaced them. Deep learning has pushed the boundaries of what is achievable in facial recognition. However, traditional computer vision methods, which had been improving for years prior to the emergence of deep learning, still have room for improvement. this study aims to compare the accuracy of facial recognition algorithms, specifically FaceNet and Eigenfaces, from deep learning and traditional computer vision domains. the availability of physical machine power and dataset scale creates a tradeoff between performance and cost. Traditional computer vision techniques may be suitable for low-power applications, while deep neural networks provide better accuracy results when high-powered machines are available. the performance benefits of deep learning outweigh the data scale and training execution time required.

关键词： Deep learning Face recognition Eigenfaces FaceNet

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Packing and Padding: Coupled Multi-index for Accurate Image Retrieval 27

Packing and Padding: Coupled Multi-index for Accurate Image ...

引用

27th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Zheng, Liang Wang, Shengjin Liu, Ziqiong Tian, Qi Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781479951178

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. the joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

关键词： Image retrieval

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Teaching robots to Predict Human Motion

Teaching Robots to Predict Human Motion

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25th IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Gui, Liang-Yan Zhang, Kevin Wang, Yu-Xiong Liang, Xiaodan Moura, Jose M. F. Veloso, Manuela Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781538680940

Teaching a robot to predict and mimic how a human moves or acts in the near future by observing a series of historical human movements is a crucial first step in human-robot interaction and collaboration. In this paper, we instrument a robot with such a prediction ability by leveraging recent deep learning and computer vision techniques. First, our system takes images from the robot camera as input to produce the corresponding human skeleton based on real-time human pose estimation obtained with the OpenPose library. then, conditioning on this historical sequence, the robot forecasts plausible motion through a motion predictor, generating a corresponding demonstration. Because of a lack of high-level fidelity validation, existing forecasting algorithms suffer from error accumulation and inaccurate prediction. Inspired by generative adversarial networks (GANs), we introduce a global discriminator that examines whether the predicted sequence is smooth and realistic. Our resulting motion GAN model achieves superior prediction performance to state-of-the-art approaches when evaluated on the standard H3.6M dataset. Based on this motion GAN model, the robot demonstrates its ability to replay the predicted motion in a human-like manner when interacting with a person.

关键词： robots Gallium nitride Generative adversarial networks Predictive models Cameras Skeleton Decoding

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Industrial 5.0 Aquaponics System Using Machine Learning techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

引用

7th International conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. the primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. the industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. this capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 IEEE.

关键词： Decision making

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate 27

Short-term Portfolio Optimization using Doubly Regularized E...

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27th International conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Guan, Quanlong He, Jinneng Lai, Zhao-Rong Hou, Yuyu Jiang, Quming Chen, Ziliang Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Chutian Dragon Co Ltd Dongguan Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Zhuhai Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. this paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. the approach uniquely combines l(1) and l(2)-regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. this algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Short-term portfolio optimization machine learning in finance application FinTech exponential growth rate approach

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3D vision for Precision Dairy Farming 6th

3D Vision for Precision Dairy Farming

引用

6th International-Federation-of-Automatic-Control (IFAC) conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： O'Mahony, Niall Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Riordan, Daniel Walsh, Joseph Inst Technol Tralee IMaR Res Ctr Tralee Co Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals this paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. this review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. these use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automation and Robotics in Agriculture Sensing and Automation in Animal Fanning

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7th Mexican conference on Pattern Recognition, MCPR 2015

7th Mexican Conference on Pattern Recognition, MCPR 2015

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7th Mexican conference on Pattern Recognition, MCPR 2015

ISBN: (纸本)9783319192635

the proceedings contain 34 papers. the special focus in this conference is on Pattern Recognition, Artificial intelligent techniques, Image Processing, Robotics and computer vision. the topics include: Recommendation of process discovery algorithms through event log classification;a new method based on graph transformation for FAS mining in multi-graph collections;classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing;a different approach for pruning micro-clusters in data stream clustering;computing constructs by using typical testor algorithms;multitask reinforcement learning in nondeterministic environments;prototype selection for graph embedding using instance selection;correlation of resampling methods for contrast pattern based classifiers;boosting the permutation based index for proximity searching;similarity analysis of archaeological potsherds using 3D surfaces;automatic detection of clouds from aerial photographs of snowy volcanoes;a comparative study of robust segmentation algorithms for iris verification system of high reliability;vision-based humanoid robot navigation in a featureless environment;evaluation of local descriptors for vision-based localization of humanoid robots;sampled weighted min-hashing for large-scale topic mining;patterns used to identify relations in corpus using formal concept analysis;improving information retrieval through a global term weighting scheme;sentiment groups as features of a classification model using a Spanish sentiment lexicon;modified binary inertial particle swarm optimization for gene selection in DNA microarray data;encoding polysomnographic signals into spike firing rate for sleep staging and patrolling routes optimization using ant colonies.

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computer vision-Based Method for Digital Twin Modelling in Railway 27

Computer Vision-Based Method for Digital Twin Modelling in R...

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27th IEEE International conference on intelligent Transportation Systems, ITSC 2024

作者： Zhang, Shirui Leromancer, Briac Nicodeme, Claire and Sncf France Sncf Sa Research and Technology Department Saint Denis France Mines Paris-PSL University and Sncf France

ISBN: (纸本)9798331505929

Efficient maintenance requires knowledge and expertise about the equipment. this is especially true for public transportation and railway, where security is one of the most important pillars. However, trains and related systems are complex, making expertise difficult to acquire. In addition, given the long lifecycle of railway equipment and infrastructures, some may no longer be documented. Yet, retrieving essential information with the required details is needed. To provide the necessary data, the authors present a novel digital twin-based concept for advanced real-world information gathering. Different modelling techniques such as LiDAR Scanning, RGB-D Scanning and Deep Learning-based scene reconstruction are investigated to create texture-aware 3D models of different railway objects. Representation quality, scale adaptation and access to technologies are discussed. From this perspective, these models are promising for object recognition, predictive maintenance, extended reality (XR) tools development and agents training in intelligent Transportation Systems (ITS). © 2024 IEEE.

关键词： Predictive maintenance

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