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International Conference on Computing, engineering, and Design (ICCED)

作者： Mahmuda Mahfuz Rimi Md Rasel Ahmed Sakhawat Hossan Md Masudur Rahaman Md Anowar Hossen Faysal Md. Ferdouse Ahmed Foysal Department of Computer Information System Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Fiber@Home Ltd Dhaka Bangladesh

ISBN: (纸本)9781665453905

The factors of population growth, unplanned urbanization, vehicles, factories, and brick kilns are constantly making air quality concerns. In addition, due to lack of public awareness, people are continually facing various kinds of unintended accidents, for example, gas cylinder poisoning. The main objective of our paper is to create a low-cost Internet of Things (IoT) based system that can measure atmospheric air quality and gas leakage. Sensitive data will be stored on the server via the Internet of Things (IoT) device, which will visualize the data from the server via the website and the Android app. Moreover, by understanding the state of the environment, the user will be informed about the environment through Android apps and websites to protect himself from the grip of air pollution and protect himself before an accident like a gas cylinder.

关键词： Atmospheric measurements Sociology Machine learning Air pollution Production facilities Pollution measurement Internet of Things

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Software-Defined Networks in IoT Ecosystems for Renewable Energy Resource Management

Software-Defined Networks in IoT Ecosystems for Renewable En...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Ugochukwu O. Matthew Renata L. Rosa Jazuli S. Kazaure Oluwaseun Jeremiah Adesina Odubola Abel Oluwatimilehin Chukwuka Michael Oforgu Olasubomi Asuni Nwamaka U. Okafor Demostenes Z. Rodriguez Department of Computer Science Federal University of Lavras Minas Gerais Brazil Department of Electrical Engineering Hussaini Adamu Federal Polytechnic Kazaure Nigeria Department of Renewable Energy System De Montfort University United Kingdom Leicester UK Computer Science/Mathematics Obafemi Awolowo University Ile-Ife Osun Nigeria Department of Data Science University of the West England Bristol United Kingdom Electrical Electronics Engineering Northumbria University Newcastle Newcastle Upon Tyne United Kingdom School of Elect. Electronics Engineering University College Dublin Dublin Ireland

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

A novel approach toward modernizing energy infrastructures and improving operational performance in distribution of solar renewable energy in smart grid is through Software-Defined Networking (SDN) technology. In addition, the global solar radiation (GSR) plays a critical role in solar power system design in order to precisely identify where solar renewable energy systems should be installed. The hourly global sun radiation measurements using SDN are required in order to precisely calculate the amount of solar energy generated, amount of energy consumed and the amount of solar energy resources conserved. In order to solve the problem of smart grid energy distribution, this study utilized artificial neural network (ANN) algorithm to build a model that predicts, on a monthly basis, the average daily global sun irradiance on a horizontal region of ground base using meteorological data which include the earth's temperature, relative humidity, wind speed, rainfall and ambient air quality. In this paper, the authors discussed the issues in implementing SDN in the energy distribution, energy demand response, energy optimization, grid resilience and real-time monitoring. By using the proposed model, the results showed a good agreement between the measured and calculated levels of global solar irradiation, demonstrating the superiority of the ANN model over empirical models due to its small noise margin. For researchers, practitioners, and policymakers looking to fully leverage SDN for advancing solar renewable infrastructures within national grid system, this research paper offers insightful information through a thorough analysis of current developments and future trends.

关键词： Temperature measurement Renewable energy sources Terrestrial atmosphere Artificial neural networks Solar energy Smart grids Solar radiation Sun Software defined networking Resilience

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K-means Clustering Powered Context Aware Food Recommender system

K-means Clustering Powered Context Aware Food Recommender Sy...

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Integrated Intelligence and Communication systems (ICIICS), International Conference on

作者： Minakshi Panwar Ashish Sharma Om Prakash Mahela Baseem Khan Department of Computer science Maulana Azad University Jodhpur Rajasthan India Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd Jaipur India Department of Electrical Engineering Hawassa University Ethiopia

This paper designed a K-means clustering powered context aware food recommender system (CAFRS). The CAFRS is based on dividing the food data sets into clusters to minimize the error between the user input and available data by computing the distance between the cluster centroid and available food data in the data set of CAFRS. Recommended food has the minimum distance from the K-means clustering (KMC) centroid. Root mean square error (RMSE) and mean absolute error (MAE) are computed to evaluate requirement of the repeat orders to meet energy demand of the user. Proposed CAFRS is effective to recommend a suitable recipe for the user and RMSE and MAE are effective to estimate the requirement of repeat order. Performance of the proposed CAFRS is superior compared to a rule based food recommender system (RBFRS). This study is performed in MATLAB environment.

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Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

Affinity-Based Power Flow Optimization in Reconfigurable Pic...

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IEEE/SICE International Symposium on system Integration

作者： Ryota Kokubu Kaito Oeda Yuusuke Kawakita Shinji Yokogawa Yoshito Tobe Haruhisa Ichikawa Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Department of Information and Computer Sciences Kanagawa Institute of Technology Atsugi Japan Info-Powered Energy system Research Center The University of Electro-Communications Chofu Japan

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity.

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Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI systems

arXiv

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arXiv 2024年

作者： Alexos, Antonios Tsai, Yu-Dai Domingo, Ian Pishgar, Maryam Baldi, Pierre Department of Computer Science University of California IrvineCA92697 United States Department of Physics and Astronomy University of California IrvineCA92697 United States IL60510 United States Department of Industrial and System Engineering University of Southern California CA90089 United States

Creating controlled methods to simulate neurodegeneration in artificial intelligence (AI) is crucial for applications that emulate brain function decline and cognitive disorders. We use IQ tests performed by Large Language Models (LLMs) and, more specifically, the LLaMA 2 to introduce the concept of "neural erosion." This deliberate erosion involves ablating synapses or neurons, or adding Gaussian noise during or after training, resulting in a controlled progressive decline in the LLMs’ performance. We are able to describe the neurodegeneration in the IQ tests and show that the LLM first loses its mathematical abilities and then its linguistic abilities, while further losing its ability to understand the questions. To the best of our knowledge, this is the first work that models neurodegeneration with text data, compared to other works that operate in the computer vision domain. Finally, we draw similarities between our study and cognitive decline clinical studies involving test subjects. We find that with the application of neurodegenerative methods, LLMs lose abstract thinking abilities, followed by mathematical degradation, and ultimately, a loss in linguistic ability, responding to prompts incoherently. These findings are in accordance with human studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Erosion

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Experimental Study of Heat Transfer Using Water - Cooled Condensers to Increase Oil Production from Plastic Waste 1

Experimental Study of Heat Transfer Using Water - Cooled Con...

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1st Paris Van Java International Seminar on computer, science, engineering, and Technology, PVJ ISComSET 2020

作者： Suryadimal Arman, Rizky Sundari, Elmi Darmayanti, Yeasy Efendi, Raimon Mechanical Engineering Department Industrial Faculty Engineering Universitas Bung Hatta Indonesia Chemical Engineering Department Industrial Faculty Engineering Universitas Bung Hatta Indonesia Accountancy Department Economic and Bisnis Faculty Universitas Bung Hatta Indonesia Information System Department Computer Science Faculty Universitas Dharmas Indonesia

This study aims to determine the performance of the heat exchanger in Low Density Polyethylene non-organic waste refining tools into fuel oil. This research is an experimental study that uses plastic waste obtained from scavengers and then cleaned, dried and chopped with a small size and then inserted into a three condenser level during a certain time. After the combustion temperature rises, water as a cooling medium is flowed in the pipe so that condensation occurs in the oil pipe. The results of the test carried out in the process of cracking plastic waste into fuel oil are affected by time, volume, temperature and heat transfer rate. Testing time for 30 minutes on the condenser obtained the heat transfer rate in the pipe 558.4 W, at 60 minutes the value of heat transfer was 732.2 W, while at 90 minutes the average heat transfer value was 1039.7 W. There was an increase in the heat transfer value as it increased Combustion time and oil produced by the reactor averaged 812 ml. © Published under licence by IOP Publishing Ltd.

关键词： Heat transfer

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Intelligent Spectrum Sensing with ConvNet for 5G and LTE Signals Identification

Intelligent Spectrum Sensing with ConvNet for 5G and LTE Sig...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Thien Huynh-The Quoc-Viet Pham Thai-Hoc Vu Daniel Benevides da Costa Van-Phuc Hoang Department of Computer and Communication Engineering HCMC University of Technology and Education Vietnam Institute of System Integration Le Quy Don Technical University Vietnam School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electrical Electronic and Computer Engineering University of Ulsan Republic of Korea Technology Innovation Institute Abu Dhabi United Arab Emirates

The paper presents an intelligent spectrum sensing approach for next-generation wireless networks by exploiting deep learning, in which we develop a deep convolutional network (ConvNet) to automatically identify Fifth Generation New Radio (5G NR) and Long-Term Evolution (LTE) signals under standards-specified channel models with diversified RF impairments. In particular, we design a semantic segmentation ConvNet to detect and localize the spectral content of 5G NR and LTE in a synthetic signal featured by spectrum occupancy. A received signal is first converted by a short-time Fourier transform and represented as a wideband spectrogram image which is then passed through the ConvNet, incorporated by DeepLabv3+ and ResNet18 to improve the accuracy of pixel-wise segmentation to further increase the accuracy of signal identification. In the simulations, our ConvNet achieves around 95% mean accuracy and 91% mean intersection-over-union (IoU) at medium SNR level and demonstrates robustness under various practical channel impairments.

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Zexiang Shao Shibin Zhang Sachin Kumar School of Cyberspace Security Chengdu University of Information Technology Chengdu China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu China Department of Computer Science and Engineering Ajay Kumar Garg Engineering College India

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally.

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Bandwidth-Efficient Two-Server ORAMs with O(1) Client Storage

arXiv

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arXiv 2025年

作者： Wang, Wei Zhang, Xianglong Xu, Peng Chen, Rongmao Yang, Laurence Tianruo School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Jinyinhu Laboratory Wuhan430040 China School of Computer National University of Defense Technology Changsha410073 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G 2W5 Canada

Oblivious RAM (ORAM) allows a client to securely retrieve elements from outsourced servers without leakage about the accessed elements or their virtual addresses. Two-server ORAM, designed for secure two-party RAM computation, stores data across two non-colluding servers. However, many two-server ORAM schemes suffer from excessive local storage or high bandwidth costs. To serve lightweight clients, it is crucial for ORAM to achieve concretely efficient bandwidth while maintaining O(1) local storage. Hence, this paper presents two new client-friendly two-server ORAM schemes that achieve practical logarithmic bandwidth under O(1) local storage, while incurring linear symmetric key computations. The core design features a hierarchical structure and a pairwise-area setting for the elements and their tags. Accordingly, we specify efficient read-only and write-only private information retrieval (PIR) algorithms in our schemes to ensure obliviousness in accessing two areas respectively, so as to avoid the necessity of costly shuffle techniques in previous works. We empirically evaluate our schemes against LO13 (TCC’13), AFN17 (PKC’17), and KM19 (PKC’19) in terms of both bandwidth and time cost. The results demonstrate that our schemes reduce bandwidth by approximately 2 ∼ 4× compared to LO13, and by 16 ∼ 64× compared to AFN17 and KM19. For a database of size 214 blocks, our schemes are over 64× faster than KM19, while achieving similar performance to LO13 and AFN17 in the WAN setting, with a latency of around 1 second. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual addresses

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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