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Towards Improving real-time Surveillance systems with Enhancement Techniques for Deep Learning Models 20

Towards Improving Real-Time Surveillance Systems with Enhanc...

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20th ieee international conference on Intelligent Computer Communication and Processing conference, ICCP 2024

作者： Vijoli, Ioana Vancea, Cristian-Cosmin Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798331539979

In the context of increasing number of public threats, the development of automated real-time detection systems, capable of identifying crime activity and potential attacks in video recordings, has become a major priority. We propose accurate and efficient deep learning solutions for analyzing surveillance camera footage and signaling situations of imminent threat, based on detecting specific elements, such as theft masks and various types of weapons. We showcase the enhancement of YOLOv8 model with an attention mechanism, based on Squeeze-and-Excitation (SE), and with deformable convolution layers. The model is trained on custom datasets with optimization algorithms like Adam, AdamW and SGD. The performance tests are employed on a separate dataset, analyzing various specific metrics. Furthermore, a comparative analysis with alternative deep learning models, such as RetinaNet and Faster R-CNN, highlights the advantages and disadvantages of each model, providing insights into their performance under various conditions. Additionally, a voting mechanism is implemented to manage discrepancies in detections from multiple models. Extensive experimental results demonstrate significant improvements in detection precision and efficiency, making the proposed enhancements a viable approach for more accurate threat detection in real-time surveillance applications, while demonstrating our model's superiority in balancing detection speed and precision. © 2024 ieee.

关键词： Deep learning

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AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT systems

AI-Driven Energy Optimization: Household Power Consumption P...

引用

2024 international conference on Microelectronics, ICM 2024

作者： Kolluru, Vinoth Kumar Challagundla, Yagnesh Chintakunta, Advaitha Naidu Roy, Bappadittya Bermak, Amine Renuka Devi, S.M. Stevens Institute of Technology Department of Data Science HobokenNJ United States University of Florida Engineering Education Dept. United States University of North Carolina Department of Computer Science and Engineering Charlotte United States School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India College of Science and Engineering Hamad Bin Khalifa Universityy Ar-Rayyan Qatar Dept. of Ece Hyderabad500104 India

ISBN: (纸本)9798350379396

Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) network models for estimating home power consumption using time-series data. The first model employs a standard LSTM technique with multivariate characteristics including global active power, voltage, and sub-metering variables. This technique strikes a balance between prediction accuracy and computing economy, making it ideal for resource-constrained applications. The second model provides an optimized version of the LSTM, which uses PyTorch and Ray Tune for hyperparameter optimization. The optimization focuses on tweaking learning rates, batch sizes, and LSTM layers to improve model accuracy and convergence speed. Using hyperparameter tuning, the Mean Squared Error (MSE) for global active power forecasts is decreased to 0.0018, proving its appropriateness for low-resource IoT systems. Both models are tested on a resampled real-world household electric power consumption dataset for effective training. The study emphasizes the advantages of multivariate time-series analysis and hyperparameter optimization, demonstrating that the optimized LSTM model can accurately predict energy consumption, enhance energy management in smart homes, and lower computing costs. Future work will look into combining more IoT data streams and real-world deployment for further improvement. The findings add to the expanding body of knowledge about energy optimization in IoT environments, addressing the crucial demand for effective, real-time energy management systems. © 2024 ieee.

关键词： Mean square error

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Research of Seismic Streaming Storage Based on Druid 23

Research of Seismic Streaming Storage Based on Druid

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Bing Chen, Jie Zou, Qijie Qin, Jing School of Information Engineering Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China School of Software Engineerin Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China

ISBN: (纸本)9781665494571

Along with the growth of seismic observation networks, the amount of seismic observation data is also increasing rapidly with the form of fast arrival stream, and it is an important and challenging task to store seismic data in time. However, two bottlenecks (slow read/write speed, poor user concurrency) still make the practical applications difficult. We proposes an Apache Druid-based storage solution for seismic observation data streams. The solution is more adaptable to the read/write requirement of large-scale seismic data streams. Specifically, we achieve real-time observation for two aspects: (I) using Kafka as a distributed message exchange system;and (II) implementing accesses and exchange with massive seismic stream data onto the Kafka indexing service provided by Druid. The experimental results show that the Druid-based temporal data storage solution has better stream input and real-time query performance compared with the existed Hbase storage method. © 2021 ieee.

关键词： Concurrent computing Smart cities time series analysis Memory Distributed databases real-time systems Task analysis

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Skeleton-embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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2023 ieee international conference on real-time computing and Robotics, RCAR 2023

作者： Luo, Li Ren, Ziliang Qin, Yong Zhang, Qieshi Gao, Xiangyang Dongguan University of Technology Department of Computer Science Dongguan52300 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Chinese University of HongKong Hong Kong Hong Kong

ISBN: (纸本)9798350327182

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained. © 2023 ieee.

关键词： Semantics

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Internet of Things and Edge computing for real time applications

Internet of Things and Edge Computing for Real Time Applicat...

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2023 ieee international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Thondebhavi Shanthakumar, Chandana Sai Harish, Neha Eshanya Giridharan, Anandi California State University SacramentoCA United States Bosch Global Software Technologies Bengaluru India TuringMinds.ai Bengaluru India Indian Institute of Science Bengaluru India

ISBN: (纸本)9781665462631

Edge computing is a networking technology focused on placing data processing closer to the data source to reduce latency and bandwidth usage. Edge computing refers to moving some operations from the cloud to more on-premises locations for example edge server, IoT device or a user's Personal Computer. The quantity of long-distance communication required between a client and server is reduced when computing is moved to the edge of the network. Edge computing is a new technique that was created to get over the drawbacks of cloud computing. It has advanced using 5g technology based on locally distributed computing, storage, and control services. The core of fifth-generation (5G) wireless networks and beyond is edge computing. The software used in simulation of the topologies is Cisco Packet Tracer (CPT) which is a multi-platform visual simulation tool created by Cisco systems that enables users to build network topologies and replicate modern computer networks. A cloud and edge computing topology model are simulated and compared for their working for processing the data, from source to the destination. Mainly focusing on low-latency of the topology. After which real time application models are created and simulated in fields of agriculture for vermicomposting, and city planning considering water logging as a major issue. © 2023 ieee.

关键词： Internet of things

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Parallel Theory-Based Risk Assessment for Networked Manufacturing systems 4

Parallel Theory-Based Risk Assessment for Networked Manufact...

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4th ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Tao, Zhikun Xiong, Gang Peng, Shaoming Shen, Zhen Yang, Fan Han, Yunjun University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Dongguan523808 China Shandong Jiaotong University School of Rail Transportation Jinan250357 China

ISBN: (纸本)9798350349252

The increasing applications of robots and other automated machines into intelligent manufacturing poses great requirement of timely risk evaluation and recognition, which is critical to the stable operation of a factory. However, only a little previous work is centered on this problem. In this paper, we present a close-loop manufacturing situation management framework base on parallel theory to address the challenge of risk recognition in real time. A graph illustrates the relationships between various factors in networked manufacturing, followed by the introduction of corresponding risk measurement indices. And then the fuzzy analytic hierarchy process (FAHP) method is applied to assess the risk of the manufacturing. The factor with the maximum risk is selected out with max-max algorithmic operators and is input into the real system for checking out. The innovation of this paper lie in the proposition of a real time risk analytic framework for operational streamlines base on parallel theory and the integration of network graph with FAHP method for risk evaluation. The numerical testing results show that the model is valid, and its corresponding evaluation method can quickly recognize the highest risk occurred in the manufacturing. The risk evaluation framework and method can be generalized to other networked and complex systems, e.g., transportation systems. © 2024 ieee.

关键词： Smart manufacturing

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Memoization-based Task Scheduling Algorithm for Smart Healthcare systems

Memoization-based Task Scheduling Algorithm for Smart Health...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Beniwala, Gunjan Singhrova, Anita Haryana India Dcrust Haryana Murthal India

ISBN: (纸本)9798350373783

The rapid increase in the number of sensing devices within the field of Internet of Things (IoT) has led to the generation of a vast amount of data on a daily basis. Cloud computing offers processing, storing capabilities, and other services like data analysis to handle such vast data volumes. However, real-time applications like online gaming, video surveillance, and the healthcare sector often require low latency and efficient bandwidth utilization, which can be challenging to achieve with traditional cloud computing. To address these challenges, Fog computing has been transpired as a solution. Fog computing extends the functionalities of the cloud computing paradigm by distributing computing resources near to the network's edge. Since Fog devices are located at the network's edge, they are often resource-constrained, making it crucial to make efficient decisions regarding job assignments and scheduling on these devices. A proficient job scheduling algorithm has the potential to decrease energy consumption, response time, and the volume of application requests. This work proposes a smart gateway framework with a proposed Dynamically Prioritized Memoization-based Best Fit Algorithm (DPMBFA) for efficient resource allocation in fog-cloud environments. We present a healthcare-based case study to schedule the task optimally to the available resources, hence increasing the Quality of Service (QoS) in IoT gateways. Latency, energy consumption, and throughput are considered for performance evaluation using YAFS, a simulation tool compared to existing approaches. The results proved that the proposed framework outperformed the existing algorithms. © 2024 ieee.

关键词： Fog computing

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Advanced Techniques for Protecting Privacy in Artificial Intelligence Powered Medical systems 4

Advanced Techniques for Protecting Privacy in Artificial Int...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Baseera, A. Scaria, Jobin M Trivedi, Abha Sharma, Mayank Sharma, Preeti Saini, Preeti Vit Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway -18 Kothrikalan Madhya Pradesh Sehore466114 India Ibs University School Of Information Technology PO Box 5181 Boroko National Capital District Port Moresby Papua New Guinea Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Bhopal-Indore Highway Sehore Madhya Pradesh Bhopal India Vit Bhopal University School of Advanced Sciences and Languages Bhopal-Indore Highway Sehore Madhya Pradesh Bhopal466114 India Bansal College of Engineering Bhopal India Manav Rachna International Institute of Research and Studies Haryana Faridabad India

ISBN: (纸本)9798331518981

Privacy protection is required when analyzing healthcare data and using it effectively. This work proposes a novel technique to secure private data in AI-powered medical systems while providing important data insights. The recommended method incorporates challenging techniques such as integrating Laplace distribution noise, managing secure data, and training group models. A privacy budget manages settings to balance analysis performance and individual contributions. The framework outperforms existing approaches in accuracy, precision, memory, F1 score, and privacy compliance. The technique improves data, models, training, and inference speeds, making it suitable for real-time healthcare applications. Iterative feedback enhances the model by modifying components based on real-world data. In addition to ensuring privacy, this entire design enables AI-powered medical systems to identify and anticipate findings. It provides a true, scalable solution that can adapt to healthcare demands, creating a new standard for AI app privacy and data usage. The technology provides a privacy-protected, highly efficient model that enhances decision-making and patient outcomes, enabling AI in healthcare. © 2024 ieee.

关键词： Differential privacy

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A Novel Transfer Learning Approach for Detecting Partial Shading in Photovoltaic systems 11

A Novel Transfer Learning Approach for Detecting Partial Sha...

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11th ieee/ACM international conference on Big Data computing, applications and Technologies, BDCAT 2024

作者： Teta, Ali Medkour, Maissa Chennana, Ahmed Chouchane, Ammar Himeur, Yassine Atalla, Shadi Mansoor, Wathiq Belabbaci, El Ouanas University of Djelfa Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates Faculty of Technology Bejaia06000 Algeria

ISBN: (纸本)9798350367300

In Photovoltaic systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems. © 2024 ieee.

关键词： Solar power generation

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Methods of Peripheral Synchronization in real-time Cyber-Physical systems 24

Methods of Peripheral Synchronization in Real-Time Cyber-Phy...

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24th international Carpathian Control conference, ICCC 2023

作者： Wiesner, Andras Kovacshazy, Tamas Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary

ISBN: (纸本)9798350310221

real-time Cyber Physical systems (RT-CPS) utilize network clock and frequency synchronization, typically ieee 1588 PTP or its derivatives, to agree on a common time and frequency on all nodes. The knowledge of common time and frequency is required for in-phase sampling and actuation in the distributed system, and for timestamping based on the global clock. Modern network enabled microcontrollers (NeMCU) and embedded application processors support (EAP) ieee 1588 in hardware;however, it means that only the physical hardware clock in the network interface controller (NIC) is synchronized, not the actual embedded peripherals, such as A/D and D/A converters, digital I/O ports, timers, PWM modules, or secondary communication interfaces. It is the task of the developer to devise a hardware and software solution for peripheral synchronization. In the paper we investigate how modern NeMCUs and EAPs support peripheral synchronization, and how one can construct synchronized peripherals utilizing the features of hardware and proper software. The findings of the paper are demonstrated by analyzing the performance of a synchronized sampling system (sound acquisition system) and an extended timestamp unit. © 2023 ieee.

关键词： Synchronization

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