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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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A Study on Formal Verification of Smart Contracts in distributed Ledger Technology 3

A Study on Formal Verification of Smart Contracts in Distrib...

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3rd IEEE international conference on Recent Advances in systems.Science and Engineering, RASSE 2023

作者： Gokulnath, G. Kizhakkethottam, Jubilant J APJ Abdul Kalam Technological University Kerala India Department of Computer Science and Engineering India

ISBN: (纸本)9798350341676

This study paper explores the critical topic of formal verification of smart contracts in distributed ledger technology (DLT) systems. Smart contracts, self-executing code running on blockchain platforms, have gained widespread adoption in various industries due to their automation and transparency benefits. However, the decentralized and immutable nature of DLT raises concerns regarding smart contract security, leading to vulnerabilities and potential exploits. Formal verification methods offer a systematic approach to analyze smart contracts for correctness and safety. This paper provides an overview of smart contracts, DLT, and the importance of formal verification. It reviews various formal verification techniques and presents state-of-The-Art tools and frameworks. The paper concludes with discussions on challenges, future research opportunities, and the role of formal verification in enhancing DLT-based applications' security and usability. © 2023 IEEE.

关键词： Smart contract

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Optimal Sizing and Allocation of distributed Energy Resources using Genetic Algorithms: Case study of the Northern-Benghazi Power 3

Optimal Sizing and Allocation of Distributed Energy Resource...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Farkash, Noureddine Farag Tahir, Ahmed Engineering University of Benghazi Department of Electrical and Electronics Benghazi Libya

ISBN: (纸本)9798350319897

The main target is to use genetic algorithms to calculate the optimal location and size of distributed Generation (DG) units for power distribution systems. The algorithm's flexibility allows engineers, electric utilities, and distribution network operators to efficiently integrate new DG units into their current distribution networks by making changes and modifications to locate the optimal DG unit and estimate its optimum size. The proposed method can also improve voltage profiles and reduce total real and reactive power losses. Tests conducted on a section of the Benghazi distribution system show that the decision-making algorithm has acceptable accuracy and is compared against results from NEPLAN software. With the increasing popularity of distributed energy systems.worldwide, this method provides solutions to many power system problems. © 2023 IEEE.

关键词： Genetic algorithms

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3rd international conference on Smart Technologies and Innovation Management, MTYMEX 2024

3rd International Conference on Smart Technologies and Innov...

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3rd international conference on Smart Technologies and Innovation Management, MTYMEX 2024

ISBN: (纸本)9783031649561

The proceedings contain 15 papers. The special focus in this conference is on Smart Technologies and Innovation Management. The topics include: Prospective Directions in the Computer systems.Industry Foundation Classes (IFC) Shaping Data Exchange for Cities Sustainability and Resilience;a Proposal of Model Combining Semantic Analysis and Knowledge Graph for Question Answering System in Vietnamese Law of Science and Technology;trends and Innovative Tactics in Startup Innovation: A Survey;interactive Web Mapping for Geolocation of Socio-community Infrastructure in a Developing Country;development of Digital Capabilities for E-commerce Agribusiness: An Exploratory Research;a Social-Software-Based Telemedicine Information System for Facilitating Healthcare Services;lab-to-Market Product Innovation: Implications of Using a Translational Research & Development Model and Methodology;a Multilayered Process Framework for Predicting Students’ Academic Performance in Open and Distance Learning;a New Notification System for Tracking Ethereum Transactions with Social Media Software;A Secure Consortium-Blockchain-Enabled Communication Scheme for DNA-Based Smart Health systems.review on Machine Learning for Zero-Day Exploit Detection and Response;A Review of AI in Spear Phishing Defense: Detecting and Thwarting Advanced Email Threats;energy-Efficient Dynamic Adaptive Encryption for Low-Resource Internet of Things.

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proceedings of the 3rd Empathy-Centric Design Workshop, EmpathiCH 2024: Scrutinizing Empathy Beyond the Individual, Co-located with ACM CHI conference on Human Factors in computing systems

Proceedings of the 3rd Empathy-Centric Design Workshop, Empa...

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3rd Empathy-Centric Design Workshop, EmpathiCH 2024

ISBN: (纸本)9798400717888

The proceedings contain 11 papers. The topics discussed include: EMiRAs-empathic mixed reality agents;empathic directions for the sounds of parenting technology: the case of baby monitors;empathy-building through personalized pixel crafting: a co-creation platform for researchers and individuals with intellectual disabilities;mENTER: co-designing an mHealth peer navigator intervention for people with disabilities;on the design risks of empathy fatigue;weight bias in design: unpacking implicit researcher beliefs for building empathy;beyond empathy: role-taking as a structural approach to participatory design;differences in physiological responses to emotional stimuli and physiological responses to walking movements while playing a walking-type VR game;and empathy-centric design in assistive technologies for cerebral palsy and disabilities: balancing aesthetics and functionality.

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Application of the ARIMA-LSTM model in temperature prediction 3

Application of the ARIMA-LSTM model in temperature predictio...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Chen, Renxing Huang, Zuyi Zhou, Zixun School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China International Business School Jinan University Zhuhai519070 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

This paper explores the application of ARIMA(AutoRegressive Integrated Moving Average) and LSTM(Long Short-Term Memory) in predicting future global temperatures in the context of climate change. The authors employ both ARIMA and LSTM models to describe past temperature trends and forecast future temperatures. Moreover, they combine the two models and consider residual analysis to establish an ARIMA-LSTM model with enhanced predictive performance. Results indicate that this model effectively predicts future global temperature changes. © 2023 SPIE.

关键词： Climate change

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Synchronization Method of Sheath and Load Current distributed Online Monitoring in Cross-Bonded Cable systems 3

Synchronization Method of Sheath and Load Current Distribute...

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3rd Power System and Green Energy conference, PSGEC 2023

作者： Zhou, Shasha Li, Lin Zhao, Zuhan Tu, Jing Zeng, Haiyan Wang, Hang Hubei University of Technology Wuhan China State Gird Wuhan Electric Power Supply Company Wuhan Electric Power Company Wuhan China

ISBN: (纸本)9798350340099

This article puts forward a method to address the issue of time synchronization in distributed current sensing for underground high-voltage (HV) cable systems. In the context, the detection of distributed sheath currents and load currents is crucial for diagnosing sheath grounding faults. To solve the problem of time synchronization without relying on external time references, the method is based on the coupling relationship between distributed sheath currents and core currents in cross-bonded high-voltage cables. It introduces a recursive-least-squares method for estimating the time delays among multiple current sensors. The estimation aims to obtain the time offset compensation that minimizes the residuals, using the short-time invariance of capacitive currents as a stability criterion. The proposed method is experimentally validated on a 220 kV high-speed railway traction transformer's primary side high-voltage cable distributed current sensing system. The experimental results demonstrate that the method converges to the point of minimum residuals, with an average residual of 0.30 A and an accurate time delay measurement of 3.93 s. Therefore, the method achieves time synchronization for distributed sheath current sensing. © 2023 IEEE.

关键词： Stability criteria

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Improving Traffic Surveillance: Deep Learning Approach for Road Anomaly Detection in Videos 3

Improving Traffic Surveillance: Deep Learning Approach for R...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Natha, Sarfaraz Arif, Muhammad Jamil, Syed Shahryar Jokhio, Fareed Ahmed Syed, Muslim Jameel Sir Syed Univ Engn & Technol Dept Software Engn Karachi Pakistan Inst Politecn Nacl Mexico City DF Mexico MAJU Karachi Pakistan Quaid E Awam Univ Engn & Technol Dept Comp Syst Engn Nawabshah Pakistan Atlantic Technol Univ Dept Enterprise & Technol Galway Ireland

ISBN: (纸本)9798350372977;9798350372984

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. This study introduces an automatic detection framework for recognizing road anomalies such as accidents, fighting, car fires, and armed snatching (gunpoint) in road surveillance videos. After reviewing the literature, the review directs that convolutional neural networks (CNNs) are a specialized deep learning approach well suited for image and video analysis. The proposed methodology combines the pre-trained CNN models with Data Augmentation (DA) techniques to fine-tune hyperparameters such as learning rate and momentum that enhance the model learning accuracy and performance for recognizing road anomalies. Furthermore, it introduced a rolling prediction algorithm to solve the flickering problem during testing and created a new road anomaly dataset (RAD) as a benchmark consisting of road surveillance videos and images. Our proposed model combined with the InceptionV3 pre-trained model achieved a best accuracy is 98.81% for detection and classification as compared to other deep learning models.

关键词： Road Anomaly Deep learning CNN InceptionV3 ResNet50 DenseNet201

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distributed computing and Data Processing Technology under the Power Data Middleware Architecture 3

Distributed Computing and Data Processing Technology under t...

引用

2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Zou, Yuelin Shen, Jia Yin, Rui Guo, Jiangtao Guo, Yi State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems.outperformed traditional power data processing systems.in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems.have certain advantages in processing power data. © 2023 IEEE.

关键词： Cloud computing Data Center System Data Processing distributed computing Electricity Data

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2023 3rd international conference on Energy Engineering and Power systems. EEPS 2023

2023 3rd International Conference on Energy Engineering and ...

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3rd international conference on Energy Engineering and Power systems. EEPS 2023

ISBN: (纸本)9798350313857

The proceedings contain 207 papers. The topics discussed include: operation and configuration analysis of a power storage system with a high proportion of wind power;economic optimal operation of integrated rural single household energy systems.an adaptability hybrid-frequency interleaved control strategy for double-frequency converters with parameter uncertainties;simulation analysis of conductive carbon black and hydrogen prepared by partial oxidation of benzene-doped acetylene;reliability evaluation of power generation system considering planned maintenance and energy storage;new technology and integrated optimization of distributed energy storage power generation;regional short-term power load forecasting considering user electricity consumption and seasonal characteristics;disturbance identification of power quality based on Markov transition field and deep residual network;and effect of discharge amplitude on electrical trees based on three-capacitance model.

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3rd international conference on Emergent Converging Technologies and Biomedical systems. ETBS 2023

3rd International Conference on Emergent Converging Technolo...

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3rd international conference on Emergent Converging Technologies and Biomedical systems. ETBS 2023

ISBN: (纸本)9789819986453

The proceedings contain 56 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical systems. The topics include: Smart Health Monitoring System for Elderly People;impact of Covid-19 and Subsequent Usage of IoT;design of Battery Monitoring System for Converted Electric Cycles;image Denoising Framework Employing Auto Encoders for Image Reconstruction;server Access Pattern Analysis Based on Weblogs Classification Methods;multilingual Emotion Recognition from Continuous Speech Using Transfer Learning;Violence Detection Using DenseNet and LSTM;financial Technology and Competitive Landscape in the Banking Industry of Bangladesh: An Exploratory Focus;on Parameterized Picture Fuzzy Discriminant Information Measure in Medical Diagnosis Problem;review on Deep Learning-Based Classification Techniques for Cocoa Quality Testing;a Curated Study on Machine Learning Based Algorithms and Sensors for Drone Technology in Various Application;automatic Detection of Coagulation of Blood in Brain Using Deep Learning Approach;deepPose: A 2D Image Based Automated Framework for Human Pose Detection and a Trainer App Using Deep Learning;Phylogenetic Study of Surface Glycoprotein (S1 Spike Protein) Sequence of SARS-CoV-2 Virus;pervasive and Wearable computing and Networks;power of Image-Based Digit Recognition with Machine Learning;open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation;enhancing Biometric Performance Through Mitigation of Sleep-Related Breaches;Neural Network Based CAD System for the Classification of Textures in Liver Ultrasound Images;fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning;a Comparative Survey on Histogram Equalization Techniques for Image Contrast Enhancement.

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