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arXiv 2024年

作者： Kang, Mingyu Chen, Duxin Pu, Ziyuan Gao, Jianxi Yu, Wenwu School of Cyber Science and Engineering Southeast University Nanjing210096 China School of Mathematics Southeast University Nanjing210096 China School of Transportation Southeast University Nanjing210096 China Department of Computer Science Center for Network Science and Technology Rensselaer Polytechnic Institute TroyNY12180 United States Frontiers Science Center for Mobile Information Communication and Security School of Mathematics Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211102 China

Counterfactual thinking is a critical yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve their performances for new scenarios. Many research works, including Potential Outcome Model and Structural Causal Model, have been proposed to realize it. However, their modelings, theoretical foundations and application approaches are usually different. Moreover, there is a lack of graphical approach to infer spatio-temporal counterfactuals, that considers spatial and temporal interactions between multiple units. Thus, in this work, our aim is to investigate a survey to compare and discuss different counterfactual models, theories and approaches, and further build a unified graphical causal frameworks to infer the spatio-temporal counterfactuals. Copyright © 2024, The Authors. All rights reserved.

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Blockchain based Security Solution for Internet of Drones

Blockchain based Security Solution for Internet of Drones

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Sudhanshu Maurya V. R. Niveditha B Anandapriya Rohan Verma Kashish Mirza Mohit Tiwari Bhimtal Campus School of Computing Graphic Era Hill University Nainital India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Application Patrician College of Arts and Science Chennai India Department of Computer Science & Engineering Hemvati Nandan Bahuguna Garhwal University Srinagar Uttarakhand India Department of Computer Science & Engineering Bhimtal Campus Graphic Era Hill University Nainital India Department of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9781665474177

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information.

关键词： Wireless sensor networks Privacy Transportation Emergency services Information and communication technology Internet of Things Reliability

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

arXiv

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arXiv 2024年

作者： Liu, Yang Lu, Jincheng Xiong, Zhongfei Wu, Fan O. Christodoulides, Demetrios Chen, Yuntian Jiang, Jian-Hua School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816-2700 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Physics University of Science and Technology of China Hefei230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Recent studies show that such collective phenomena can be effectively described by a Rayleigh–Jeans thermodynamics theory which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for the nonequilibrium transport and fluctuations is yet to be established. Here, we employ the full counting statistics theory to study the nonequilibrium transport of particle and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperature to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. Our work seeks to deepen the understanding of irreversible non-equilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics. © 2024, CC BY-NC-ND.

关键词： Temperature

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical Engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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Role and application of Ribs in Solar Air Heater

Role and Application of Ribs in Solar Air Heater

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Amandeep Singh Sanjeev Kumar Joshi Dharmendra Kumar Rajiv Kumar Shiv Dayal Pandey Madhur Thapliyal Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand India Computer Science and Engineering Department Shivalik College of Engineering Dehradun Uttarakhand India Department of Computer Application Graphic Era Hill University Dehradun Uttarakhand India

Maintaining the clean environment and reducing the usage of fossil fuels requires switching to renewable sources of energy, because of limited resources of fossil fuels and having insufficient fossil fuel storage. The world is facing from shortage of fossil fuel and its cost is going too high and its smoke is very harmful to the environment. In this crisis, the world is starting the use of renewable energy. Renewable energies are easily available, and it reduces air pollution and fuel cost. non-conventional energy sources, like solar energy, atomic energy, hydroelectric power, wind energy etc. are a substitute for conventional energy. Solar energy is the best one, is used in various utilization, like solar air heaters, solar water heaters, solar power generation, solar pumps, solar cooking etc. Solar air heater that absorbs heat from the sun's radiations to heat air and it is collected by the collector & converted into kinetic energy and also restored in the form of potential energy. In this review, we will discuss the role of ribs in solar air heating systems to enhance the heat transfer rate, types of rib, and the purpose of ribs arrangement in solar air heating systems. Ribs are used to transfer maximum heat and increase the pressure inside of the cabin and it located on the absorber plate surface and it help to increase the solar air heaters performance. This study emphasizes exploring the effects of ribs and the behaviour of fluid after impacting on the ribs. For drying crops, heating buildings, and other uses, solar air heaters are extremely important in the agriculture sector, small-scale industries, in urban and villages for heating and drying purposes, etc. The use of ribs can enhance the effectiveness of a solar air heating system and this technology can be used for heat transmission enhancement, power generation & can fulfil the world's energy requirements. The primary objectives of this review article are to assist and summaries the research work carried

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Deep Global Distance Estimation Hashing for Image Retrieval

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IEEE Transactions on Big Data 2025年

作者： Xu, Mengfei Luo, Bowen Xu, Jianfeng Ding, Feng The School of Software Nanchang University Jiangxi330047 China Department of Computer Science University of Regina ReginaSKS4S 0A2 Canada Nanchang Key Laboratory for Innovative Application of Information Technology China

Hashing technology has an excellent high dimensional visual information encoding ability. It can map distance information from high dimensional to low dimensional Hamming space, which is widely used in large-scale image nearest neighbor search tasks. Most recent studies, such as pair-wise and triplet-wise, use local positional relations to construct hashing functions. These learning methods, which are only based on local information, will lead to an incoherent feature representation, especially for semantic similarity features. Therefore, we propose a global distance estimation hashing (DEH). The DEH establishes a deep hashing model by constructing global distance relations that can define a distance between every paired category. Thus the distance relations constructed by DEH include much more detailed information. In other words, DEH focuses on global distance relations and makes the division of boundaries have excellent performance, especially for those samples with subtle changes. After the mapping, the model quantization approach effectively decreases the quantization loss caused by transitioning from the real-valued space to the Hamming space. Numerous experiments show that our DEH achieves excellent results on CIFAR-10, NUS-WIDE and ImageNet. These achievements not only validate the effectiveness of DEH in general image hashing retrieval tasks but also demonstrate its significant advantages in handling the large-scale multimedia retrieval systems. © 2015 IEEE. All rights reserved.

关键词： Content based retrieval

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： Polynomial regression

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A Novel Approach to Identify the Anomaly Detection in Electricity usage based on Machine Learning Algorithms and Big Data 3

A Novel Approach to Identify the Anomaly Detection in Electr...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Panneerselvam, M. Neela, K. Rajeshwari, R. Vengadapathiraj, M. Sobitha, S. Mohanavel, V. Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Comprehension mistakes and frauds may both be detected via the analysis of advanced metering information. By means of on-site examinations or even other techniques, the energy suppliers will be made aware of any suspect usage patterns by this study. The analysis of usage patterns using Machine Learning (ML) techniques may reveal errors, measuring interruptions from assaults, or fine motor control with utility companies. Another of the basic instances of an anomaly detection is fraud detection since it is difficult to categories transactional or utilization information. Additionally, scams vary in type, making it difficult to consistently learn from them. This research study presents an unsupervised ML method to identify out-of-the-ordinary qualities in time series, set a threshold for the proportion of out-of-the-ordinary readings towards the overall measurements, and then classify the moving average as questionable or not. Initially, two different methods are provided to find the differences in time-series based data streaming: Spectral Residual-Convolutional Neural Network and anomaly trained model based on martingales. The properly established information is then subjected to Fisher Linear Discriminant analysis and Two-Class Boosted Decision Tree applications. © 2022 IEEE.

关键词： Large dataset

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