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Searching for explanations of black-box classifiers in the space of semantic queries

Semantic Web 2022年第4期1卷 1-42页

作者： Liartis, Jason Dervakos, Edmund Menis-Mastromichalakis, Orfeas Chortaras, Alexandros Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results. © 2023 – The authors.

关键词： description logics Explainable AI (XAI) explanation rules knowledge graphs opaque machine learning classifiers post-hoc explainability reverse query answering semantic query answering

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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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Science China(Technological Sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Detecting Patent Ductus Arteriosus in Neonatal Phonocardiograms 50

Detecting Patent Ductus Arteriosus in Neonatal Phonocardiogr...

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50th Computing in Cardiology, CinC 2023

作者： Zamani, Mohammad Ali Grooby, Ethan Lacey, James Malhotra, Atul Marzbanrad, Faezeh School of Electrical and Computer Engineering University of Tehran Iran Monash University Department of Electrical and Computer Systems Engineering Australia University British Columbia Department of Electrical and Computer Engineering Canada Monash University Monash Children's Hospital and Department of Paediatrics Australia

ISBN: (纸本)9798350382525

During fetal development, ductus arteriosus allows blood ejected by the right ventricle to bypass the lung. The persistence of this ductus after birth however is considered pathological and is termed patent ductus arteriosus (PDA). Using phonocardiograms (PCGs) acquired from a digital stethoscope, the objective is to explore discriminatory features and statistically analyse them. In this research, we obtained 48 PCG recordings from 41 neonates. 17 recordings (15 neonates) had PDA, 8 recordings (7 neonates) had other heart structure abnormalities identified during echocardiography and were excluded in this study, and 23 recordings (21 neonates) had no heart structure abnormalities based on clinical assessment. Recordings were denoised using non-negative matrix co-factorization. Denoised phonocardiograms were then segmented into S1, systolic, S2, and diastolic periods. Temporal (durations and maximum values), statistical (variance, skewness, and kurtosis), and power (total power, and 25-100Hz, 25-45 Hz, 45-80 Hz, 100-200 Hz, and 200-400Hz band powers) were obtained for each heart segment and relative ratios between segments calculated. Statistical tests were then performed to identify significant features for discriminating PCG recordings. © 2023 CinC.

关键词： Echocardiography

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Nonlinearity Estimation and Compensation for Accurate PMSM Modeling and Voltage Prediction

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CES Transactions on electrical Machines and systems 2024年第4期8卷 394-403页

作者： Beichen Ding Yuting Lu Chunyan Lai Weiwen Peng Kaide Huang Guodong Feng IEEE School of Advanced Manufacturing Sun Yat-sen UniversityShenzhen 510275China School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhen 510275China Department of Electrical and Computer Engineering Concordia UniversityMontrealCanada School of Mathematics and Big Data Foshan UniversityFoshan 528000China

For permanent magnet synchronous machine(PMSM),the machine model is critical to predict the operating states for motor control,which,however,can be greatly affected by system ***,this paper investigates accurate machine modeling for control and parameter *** the proposed approach,the PMSM model with saturated inductances is used as the base model,and this paper investigates modeling and compensation of the offsets to the base model due to system nonlinearities such as saturation and core loss effects for accurate machine modeling and voltage ***,the offsets to the base model are modeled using nonlinear functions with variable coefficients to compensate saturation and core loss effect,which can achieve better accuracy without changing the model structure.A differential estimation model is derived to estimate the model coefficients from a small amount of measurements with simplified ***,the model offset calculation is both computation and memory efficient with simplified *** contribution is to improve the machine model accuracy and achieve precise voltage prediction for practical ***,comparisons and the application to temperature estimation are conducted on a test interior PMSM to validate the proposed approach.

关键词： Core loss Differential modeling Multiparameter estimation PMSM

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Self‐training maximum classifier discrepancy for EEG emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1480-1491页

作者： Xu Zhang Dengbing Huang Hanyu Li Youjia Zhang Ying Xia Jinzhuo Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqingChina School of Electrical and Computer Engineering Inha UniversityIncheonSouth Korea School of Software Yunnan UniversityYunnanChina

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming *** study proposed to solve the limited label problem via domain adaptation ***,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this *** proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'***,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is ***,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is *** experiments on SEED and SEED‐IV are *** experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

关键词： artificial intelligence bioinformatics domain adaptation EEG neural network pattern classification

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D-scheduler:A scheduler in time-triggered distributed system through decoupling dependencies between tasks and messages

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Science China(Technological Sciences) 2024年第1期67卷 183-196页

作者： YANG TingTing ZHANG YuQi YUE FengLai WUNIRI QiQiGe TONG Chao School of Computer Science&Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China National Innovation Center of Intelligent and Connected Vehicles Beijing 100176China

Time-triggered architecture,as a mainstream design of the distributed real-time system,has been successfully applied in the aerospace,automotive and mechanical ***,time-triggered scheduling is a challenging NP-hard *** are few studies that could quickly solve the scheduling problem of large distributed time-triggered *** solve this problem,a communication affinity parameter is defined in this paper to describe the degree of bias of the shaper task towards sending or receiving *** on this,an innovative task-message decoupling model named D-scheduler is built to reduce the computation complexity of the scheduling problem in large-scale ***,we provide mathematical proof that our model is a convex optimization that is easy to solve with existing computational *** experiments substantiate the efficacy of the *** dramatically reduces the scheduling complexity of large-scale real-time systems with a small loss of solving space compared to the federal scheduler.

关键词： time-triggered architecture time-triggered scheduling communication affinity parameter task-message decoupling model

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel Robot 13

Practical Implementation of Real-Time Waste Detection and Re...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Jalali, Hasan Garjani, Shaya Kalhor, Ahmad Masouleh, Mehdi Tale Yousefi, Parisa University of Tehran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran Imam Reza International University School of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350330151

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-label dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent;however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform. © 2023 IEEE.

关键词： Classification (of information)

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A Two-Stage Stochastic Techno-Economic Optimal ESS Sizing Model to Enable Maximum Exploitation of RESs

A Two-Stage Stochastic Techno-Economic Optimal ESS Sizing Mo...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Mohseni-Bonab, Seyed Masoud Alizadeh, Ali Kamwa, Innocent Rabiee, Abbas Digital Systems Hydro-Quebec Research Institute School of Electrical and Computer Engineering College of Engineering Laval University QC Canada School of Electrical and Computer Engineering College of Engineering Laval University QC Canada University of Zanjan Iran

ISBN: (纸本)9798350396782

Renewable Energy Sources (RESs) are gaining traction in active distribution systems due to their cost-effectiveness and the imperative for energy transition. However, their integration poses technical hurdles, including reverse power flow (RPF) and power balance issues, potentially resulting in energy curtailment. Energy Storage systems (ESSs) have emerged as a pivotal technology to mitigate these challenges by storing energy from RESs and harmonizing their output power with technical constraints. This paper introduces a two-stage techno-economic stochastic model for optimizing ESS sizing, allowing for the maximization of installed RESs capacity while considering their stochastic behavior. Investors can leverage this model to enhance profits by factoring in the economic aspects of ESSs and the potential for renewable energy installation, leading to an increase in revenue. © 2023 IEEE computer Society. All rights reserved.

关键词： energy storage systems monetary value analysis Renewable energy sources two-stage stochastic model

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