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Forecasting Customer Baseline Load Based on Similar Electricity Usage Patterns

Forecasting Customer Baseline Load Based on Similar Electric...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Kanya Pliansaad Duangjai Jitkongchuen Panita Thusaranon Electricity Generating Authority of Thailand (EGAT) Bangkok Thailand Big Data Institute (Public Organization) Ministry of Digital Economy and Society Bangkok Thailand College of Innovative Business and Accountancy Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

In incentive-based demand response programs, compensation is calculated and paid to participants who adjust their electricity usage as requested. This is based on the difference between the Customer Baseline Load (CBL) the estimated electricity usage without participating in the program and the actual load during the event. Accurate CBL calculation ensures fair compensation, incentivizing participation. This research develops a model to forecast CBL using K-Means clustering, incorporating Dynamic Time Warping (DTW) as a distance metric. The centroid from each cluster represents the CBL for individual customers within the cluster. This approach is compared to the traditional "Max 4/5" method. Results show the centroid-based CBL calculation aligns more closely with actual electricity usage, with a relative root mean square error (RRMSE) of 0.175 compared to 0.312 for the Max 4/5 method.

关键词： Accuracy Electricity Electric variables measurement Predictive models Demand response Root mean square Forecasting Testing Load modeling

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A Method for Analyzing Data in Measuring Devices Using a Specialized Computing System for Synthesizing and Evaluating Statistical Hypotheses

A Method for Analyzing Data in Measuring Devices Using a Spe...

引用

international conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ilya Tarasov Laboratory of Specialized Computer Systems RTU MIREA Moscow Russia

The article examines the application of a method for processing experimental data in measurement systems by generating hypotheses about the presence of a given set of parameters and estimating their probability. The difference between the method is the use of the error probability distribution function with an independent parameter "distribution scale" and the search for a combination of process model parameters at which the posterior probability function takes the maximum value. The method is characterized by high resistance to impulse noise, as a result of which it can be used in measuring systems. A special feature of the method is the large number of calculations required to generate hypotheses and test their probability. To solve this problem, a parallel computing system architecture has been proposed that generates hypotheses and evaluates them using independent processing elements.

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Analyzing the Impact of Domain Similarity: A New Perspective in Cross-Domain Recommendation

Analyzing the Impact of Domain Similarity: A New Perspective...

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international joint conference on Neural Networks (IJCNN)

作者： Ajay Krishna Vajjala Arun Krishna Vajjala Ziwei Zhu David S. Rosenblum Department of Computer Science George Mason University Fairfax VA USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cross-domain recommendation (CDR) has recently emerged as an effective way to alleviate the cold-start and sparsity issues faced by recommender systems, by transferring information from an auxiliary domain to a target domain to improve recommendations. Studying the similarity between domains is a novel direction in CDR research, potentially opening doors for further exploration. In this context, we introduce a systematic approach to quantify similarity between a pair of domains and explore how current CDR methods perform with both similar and dissimilar domain combinations. We achieve this by presenting two original similarity metrics. Our extensive empirical evaluation on different domain combinations demonstrates that the state-of-the-art CDR algorithms do not perform significantly better when using source domains that are more similar to the target domain, compared to those that are less similar. Importantly, we find that no matter how similarity is measured, it does not correlate with the recommendation performance of the state-of-the-art algorithms.

关键词： measurement Systematics Neural networks Recommender systems

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Novel Hybrid One-Class RNN Model Using Adversarial Data Augmentation for Anomaly Detection in systems Operations

Novel Hybrid One-Class RNN Model Using Adversarial Data Augm...

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international conference on Advanced Computing and Communication systems (ICACCS)

作者： Jaisharma. K Devi. T N. Deepa Dept. of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The industries are expanding their infrastructure landscape with advanced processors and supercomputers. It is also important to maintain the systems operations robustly and efficiently to handle the service with zero interrupts. But the illegal contractors will be tampering the system's ability by using augmented data dynamically. This research is to detect the anomaly presence through system operations data. The proposed Novel Hybrid One Class RNN model utilizes the advertiser augmented data to compare with the actual system operations data such as processor utilization, data read/write, network utilizations, request/response count, systems temperature and latency measure over the time period. The result shows the Novel Hybrid One Class RNN model achieves the accuracy of 98.4%, precision of 98.1%, recall of 97.8% and F1-score of 97.3% and performed better than the existing models such as K-mean, Gaussian, Markov Chain, Isolation Forest and One Class SVM models for the considered real world AWS EC2 operations dataset collected under various work load conditions.

关键词： Temperature measurement Program processors Accuracy systems operation Computational modeling Training data Predictive models Data models Anomaly detection Load modeling

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Application of Geometric Invariants in the Problem of Trajectory Tracking for UAVs

Application of Geometric Invariants in the Problem of Trajec...

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2022 international Siberian conference on Control and Communications, SIBCON 2022

作者： Khachumov, Mikhail Ailamazyan Program Systems Institute of RAS Intelligent Control Laboratory Pereslavl-Zalessky Russia Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665476287

The study discusses approaches and an apply example of using geometric invariants in problems of control theory. An analytical review of modern papers in the field of trajectory tracking and path following control problems for unmanned aerial vehicles (UAVs) has been carried out. To increase the autonomy and ability to cope with uncertainties, it is proposed to use the theory of intelligent-geometric control. The problem of the trajectory tracking is considered in the framework of the pursuit-evasion differential game. Control invariants are considered in the form of ellipsoids, that can be used, for example, in pursuit-evasion problems. The task of determining the parameters of the pursuer's motion to meet the evader on an ellipsoid is solved. The dependence of the pursuer's speed on the parameters of the ellipse is shown. A modeling scheme is proposed aimed at joint application of optimal and intelligent control methods to solve trajectory tracking problem in difficult conditions. Simulations carried out in a disturbed environment on controlling the motion of a UAV group showed prospects of the proposed approach. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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AI-Driven Real-Time Exercise Repetition Counting System

AI-Driven Real-Time Exercise Repetition Counting System

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international conference on Signal Processing and Communication (ICSC)

作者： Ratan Lal Rikriti Koirala Sai Swathi Thota Khaja Nayab Rasool Shaik School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Faculty in the school of computer science and information systems Northwest Missouri State University USA School of computer science and information systems at Northwest Missouri State University USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Real-time exercise detection is a challenging problem. Hence, we present a real-time exercise detection system that addresses the challenge of accurately monitoring exercises without the need for an expensive, real-life coach. Our approach leverages machine learning techniques to detect errors in exercises and provide actionable feedback, making fitness training more accessible and affordable. First, we collect a dataset, capturing multiple video frames with joint landmarks represented as key points in each image. These images are labeled with exercise actions such as curls, presses, and squats. Then, we perform data cleaning and splitting. After that we have trained and evaluated two machine learning models: Long Short-Term Memory (LSTM) networks and Random Forest based on metrics, namely, accuracy, precision, recall, F1 score. From our experimental results, we have found that the LSTM model outperforms the Random Forest, demonstrating its superior effectiveness in real-time exercise detection. Our results highlight the potential of AI-driven systems to provide personalized fitness guidance, opening doors to innovative solutions in the health and fitness industry.

关键词： Training measurement Industries Presses Streaming media Signal processing Real-time systems Long short term memory Random forests Monitoring

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An Effective Implementation of Activation Functions in Nodule Classification

An Effective Implementation of Activation Functions in Nodul...

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international conference on Advanced Computing and Communication systems (ICACCS)

作者： Sijo Thomas Sajimon Abraham Praveen Kumar Faizal Basheer School of Computer Sciences Mahatma Gandhi University Kottayam India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The categorization of lung nodules is crucial for promptly identifying and evaluating lung-related illnesses. This research explores the efficient utilization of activation functions in classifying nodules within CT scan images. The study compares model performances with both standard benchmarks and a custom-built 3D Convolutional Neural Network (3D CNN). The effectiveness of these models is assessed by comparing their performance metrics, such as accuracy and ROC curve (receiver operating characteristic curve). Our custom build model is a fine tuned model in the aspect of model architecture, data normal-ization data augmentation and other hyper tuning parameters except activation functions. Model performance will be evaluated on the basis of accuracy on the implementation of activation functions - Tanh, ReLU (Rectified Linear Unit), softmax, Leaky ReLU (Leaky Rectified Linear Unit) etc.

关键词： Solid modeling Accuracy Three-dimensional displays Computational modeling Computed tomography Data models Loss measurement Convolutional neural networks Tuning Standards

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Refinery: Graph Solver as a Service Refinement-based Generation and Analysis of Consistent Models

Refinery: Graph Solver as a Service Refinement-based Generat...

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international conference on Software Engineering - Companion, ICSE, Companion

作者： Kristóf Marussy Attila Ficsor Oszkár Semeráth Dániel Varró Department of Measurement and Information Systems Budapest University of Technology and Economics Budapest Hungary Department of Computer and Information Science Linkoping University Linküping Sweden Department of Electrical and Computer Engineering McGill University Montreal Canada

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https: $I$ Iyoutu. be/Qy_3udNsWsM Continuously deployed at: https: $I$ Irefinery. servicesl

关键词： Analytical models Systematics Computational modeling computer architecture Mathematical models Specification languages Logic

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Biomimetic Convergence-Based High Resolution Depth Estimation

Biomimetic Convergence-Based High Resolution Depth Estimatio...

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international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Achintya Harsha Prashanth Jonna Nitheezkant R Madhav Rao IIIT Bangalore

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Accurate depth estimation is a fundamental challenge in robotics and computer vision, crucial for applications ranging from autonomous navigation to complex object manipulation. While traditional techniques such as RGBD cameras and 3D Lidar systems provide high precision, their high costs and computational demands restrict their use in many scenarios. Conversely, monocular depth estimation (MDE) offers a more affordable solution but often struggles with depth accuracy and real-time performance. In response, this research introduces a novel convergence-based stereo vision system that leverages two independently movable cameras for dynamic depth estimation. The system facilitates depth tracking of any selected object by adapting camera orientation and extrinsic matrices to achieve millimeter-level accuracy with less than 1 % error in depth estimation over 5 meters, which is significantly better than few state-of-the-art Stereo systems that offer up to 2% error over a similar range. ArUco tag-based calibration mechanism was used to improve accuracy due to precise angle calculation. Further, the system successfully tracks and localizes multiple human body joints simultaneously using the Google Mediapipe joint Detection model, providing a 3D approximation of the subject's position concerning the system in real-time. This approach not only offers a cost-effective and precise solution for real-time depth perception but also opens new avenues for dynamic, high-resolution environmental interaction in robotics.

关键词： Meters Solid modeling Accuracy Three-dimensional displays Laser radar Depth measurement Robot vision systems Cameras Real-time systems Stereo vision

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On the Characterization of Pareto Boundary for joint Communication and Sensing in MIMO Wireless systems

On the Characterization of Pareto Boundary for Joint Communi...

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IEEE international conference on Communications Workshops, ICC

作者： Thakshila Perera Amine Mezghani Ekram Hossain Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

A joint communication and sensing (JCAS) system is considered with a user for downlink communication and a sensing target of interest using a multi-input multi-output (MIMO) terrestrial base station (BS). We provide a MIMO joint beamforming design for joint communication and sensing functionalities. As performance metrics, we use the mutual information (MI) and the Fisher information (FI) for the communication user and the sensing object, respectively. Our main objective is to characterize the MI/FI Pareto boundary of a JCAS system. To this end, we propose an optimization problem to maximize the weighted sum of the FI and the MI while satisfying the transmit power budget at the BS. We develop a closed-form solution up to one scalar (to be found numerically) for the single-user, single-sensing object scenario while achieving a trade-off between communication and sensing. Analytical and numerical results show that, for JCAS, joint beamforming is better than separate independent beamforming. The trade-off reveals differences in the performance of JCAS with the user's and the target object's angles of departure (AoD), as well as the number of transmitting and receiving antennas at the BS.

关键词： Wireless communication measurement Array signal processing conferences Transmitting antennas Receiving antennas Sensors

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