The interest in software as tools to assist speech therapy has grown in recent years, with proposed features primarily focused on the analysis of children’s speech. However, there is still a gap in tools to apply pho...
详细信息
Real-time control of complex process equipment is a crucial aspect for product quality assurance in the flow shop. However, there are two bottleneck problems, including the inaccurate quality prediction and the unstab...
详细信息
The proceedings contain 27 papers. The special focus in this conference is on Information and Software Technologies. The topics include: Access control Approach for controller Management Platforms;Leveraging Sema...
ISBN:
(纸本)9783031489808
The proceedings contain 27 papers. The special focus in this conference is on Information and Software Technologies. The topics include: Access control Approach for controller Management Platforms;Leveraging Semantic Search and LLMs for Domain-Adaptive Information Retrieval;synergizing Reinforcement Learning for Cognitive Medical Decision-Making in Sepsis Detection;towards data Integration for Hybrid Energy System Decision-Making processes: Challenges and Architecture;modelling Normative Financial processes with process Mining;sentiment analysis of Lithuanian Youth Subcultures Zines Using Automatic Machine Translation;chatbots Scenarios for Education;understanding User Perspectives on an Educational Game for Civic and Social Inclusion;using Quantum Natural Language processing for Sentiment Classification and Next-Word Prediction in Sentences Without Fixed Syntactic Structure;Investigation of the Statistical Properties of the CTR Mode of the Block Cipher Based on MPF;analyzing the Impact of Principal Component analysis on k-Nearest Neighbors and Naive Bayes Classification Algorithms;Comparison of kNN Classifier and Simple Neural Network in Handwritten Digit Recognition Using MNIST database;comparison of Support Vector Machine, Naive Bayes, and K-Nearest Neighbors Algorithms for Classifying Heart Disease;Iterative Method of Adjusting Parameters in kNN via Minkowski Metric;predicting Diabetes Risk in Correlation with Cigarette Smoking;soft Inference as a Voting Mechanism in k-Nearest Neighbors Clustering Algorithm;The BLDC Motor Efficiency Improvement by Electronical Correction of the Power States Indications;The Impact of Entropy Weighting Technique on MCDM-Based Rankings on Patients Using Ambiguous medical data;Online PID Tuning of a 3-DoF Robotic Arm Using a Metaheuristic Optimisation Algorithm: A Comparative analysis;android Malware Detection Using Artificial Intelligence;autoencoder as Feature Extraction Technique for Financial Distress Classification.
The current objectives for the energy transition in Germany strive for a substantial growth in decentralized generation based on renewable energies, alongside the decarbonization of the transportation and heating sect...
详细信息
In order to solve the problem of oil and gas recovery and treatment, better control the condensing temperature of the low-temperature heat exchanger, so that the exhaust gas and oil and gas recovery rate can meet the ...
详细信息
In recent years, multiple techniques have been proposed to defend computing systems against control-oriented attacks that hijack the control-flow of the victim program. data-only attacks, on the other hand, are a less...
详细信息
ISBN:
(纸本)9781450393386
In recent years, multiple techniques have been proposed to defend computing systems against control-oriented attacks that hijack the control-flow of the victim program. data-only attacks, on the other hand, are a less common and more subtle type of exploit which are more difficult to detect using traditional mitigation techniques that target control-oriented attacks. In this paper we introduce a novel methodology for the detection of data-only attacks through modeling the execution behavior of an application using low-level hardware information collected as a data series during execution. One unique aspect of the proposed methodology is that it uses a compilation flag based approach to collect hardware counts, eliminating the need for manual code instrumentation. Another unique aspect is the introduction of a data compression algorithm as the classifier. Using several representative real-world data-only exploits, our experiments show that data-only attacks can be detected with high accuracy using the proposed methodology. We also performed analysis on how to select the most relevant hardware events for the detection of the studied data-only attack, as well as a quantitative study of hardware events' sensitivity to interference.
This paper presents a comprehensive study on the impacts of false data injection attacks on microgrid operation, and introduces a hybrid approach to ensure stability and reliability for diverse scenarios. Two realisti...
详细信息
ISBN:
(数字)9798350318555
ISBN:
(纸本)9798350318562
This paper presents a comprehensive study on the impacts of false data injection attacks on microgrid operation, and introduces a hybrid approach to ensure stability and reliability for diverse scenarios. Two realistic attack models are developed, targeting the load profiles and renewable generation data respectively. Simulation results reveal potential load balance discrepancies during islanded microgrid operation under these attacks. To mitigate these challenges, the proposed hybrid approach integrates optimization-based energy management with adaptive control schemes, ensuring stable microgrid operation in various conditions.
As the penetration rate of new energy increases, the interactions between new energy power stations and grid are becoming stronger. GB 38755-2019 "Code on security and stability for power system" clarifies n...
详细信息
Target positioning has always been a hot research topic in the field of sensors. In recent years, radar detection technology has become increasingly mature, resulting in a large number of radar sensors have begun to b...
详细信息
Deep learning methods can extract reliable feature representations from massive processdata to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and eve...
详细信息
ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
Deep learning methods can extract reliable feature representations from massive processdata to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. This information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. The method is validated in a debutanizer process. The results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER).
暂无评论