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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Otaru, Musa Adinoyi, Abdulkareem Ajiya, Mohammed Aljlayl, Mohammed Yanikomeroglu, Halim Bayero University Kano Department of Electrical Engineering Nigeria Technology Strategy Architecture Saudi Telecommunications Company Saudi Arabia Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665412629

We use fixed relays deployed by network operators to reduce re-transmission (thereby reducing network power requirements) in addition to providing excellent end-to-end error performance in a revisit to automatic repeat request-based cooperative relaying. The new relaying scheme facilitates the creation of a connectivity suitable for delay tolerant internet-of-things (IoT)-type services. For IoT devices, link reliability and power efficiency are a major system design consideration. The proposed scheme operates in the following fashion. The source (e.g., a sensor) transmits for a certain time window. During this period, the source and the relays do not require per transmission acknowledgment (ACK) from the destination. At the end of a transmission window, the destination sends a 'group' ACK. If negative, relays are invited to help. Depending on the cooperation strategy, each relay transmits in either one transmission time slot or a few time slots, using a suitably chosen higher-order modulation constellation. If no error occurs, the source continues its transmission. Furthermore, we devise a novel strategy for selecting the best relay for this new way of exploiting the benefits of multiple relays in a network. In the process, we reclaim the bandwidth expansion that comes with multi-relay system but still maintaining E2E bit error rate that is superior to that of a single relay cooperation. Numerical results that reveal the benefits of the new and modified ARQ cooperative relaying scheme are presented. © 2021 IEEE.

关键词： Energy efficiency

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection 5

Recommendation System Towards Residential Energy Saving Base...

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Atalla, Shadi Himeur, Yassine Mansoor, Wathiq Amira, Abbes Fadli, Fodil Copiaco, Abigail Sohail, Shahab Saquib University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah Department of Computer Science Sharjah United Arab Emirates Qatar University Dept. of Architecture & Urban Planning Doha2713 Qatar Jamia Hamdard School of Engineering Sciences & Technology New Delhi110062 India

ISBN: (纸本)9781665492652

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users' activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors' algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size. © 2022 IEEE.

关键词： Electric power utilization

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Reconstruction of the Mosul Old City by Procedural Modelling

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SSRN 2024年

作者： Hussein, Khalid Ahmed Ismaeel, Emad Hani Ihsan, Rasheed Rebar Sadeeq, Haval Tariq Architecture Engineering Department College of Engineering Nawroz University KRD Duhok Iraq Dept. of Architecture Coll. of Engineering University of Mosul Iraq Computer and Communication Department College of Engineering Nawroz University KRD Duhok Iraq Information Technology Department Technical College of Duhok Duhok Polytechnic University KRD Region Iraq

The old city of Mosul possesses a rich built heritage, featuring distinct architectural styles evolved, incorporating indigenous knowledge and expertise to address functional, climatic, economic, and construction requirements. Tragically, during the military operations in 2016-2017, Mosul Old City experienced extensive devastation, resulting in the loss of numerous landmarks and the irreplaceable built heritage of this city. However, digitization has emerged as a potent heritage preservation and restoration tool. It serves as a vital initial step in reviving deteriorated heritage by providing invaluable information to specialists. In light of this, the present study aims to regenerate the urban facades of Mosul's old city automatically, ensuring they retain their original characteristics. This objective will be accomplished by utilizing procedural modelling techniques and algorithms incorporating specific characteristics as constraints within the regeneration process. The research makes a notable contribution to digital architectural design by employing Shape Grammar analysis to derive rules. Subsequently, these rules are implemented through Python to facilitate the automatic generation of 750 heritage facade designs. Ultimately, the study demonstrates the feasibility of using procedural modelling to recreate the decimated urban fabric, ensuring the restored structures exhibit the same formal and structural characteristics as their predecessors. © 2024, The Authors. All rights reserved.

关键词： Python

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Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers

Condition monitoring of industrial exhaust motor-fan assembl...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Abhrodeep Das Ayushman Agrawal Nishkarsh Kundoliya Ananta Dutta Surjya K Pal Rishi Kumar Dept. of Computer Science and Engineering Jalpaiguri Government Engineering College WB India Dept. of Mechanical Engineering Indian Institute of Technology Kharagpur WB India Dept of Ocean Engg & Naval Architecture Indian Institute of Technology Kharagpur WB India Research & Development Centre for Iron & Steel Steel Authority of India Limited (SAIL) Ranchi Jharkhand India

Exhausters are very crucial components in sinter plant of steel manufacturing industries. Problems in fan or motor side of the exhausters lead to machine stoppages or reduced speed, resulting in stoppage of the system and decreased sinter production. Common issues include imbalance, misalignments, looseness, eccentricity, and motor bearing failure. The proposed IoT-based system aims to continuously analyze vibration signals, predict issues, and prevent sudden stoppages and exhauster tripping, thereby enhancing overall plant efficiency. This study focuses on online monitoring and health prediction of industrial exhausters by selection of statistical features extracted from triaxial time-series vibration data by using multiple optimizers, followed by implementation of machine learning classifiers. By incorporating a novel feature selection process based on nature-inspired optimizers, this methodology aims to enhance the overall generalization performance of classification. The retrained models with cross-validation were evaluated with real-time industrial data, which shows 99.5% cross-validation accuracy and 95.83% real-time prediction accuracy. The proposed methodology holds great potential for detecting machine faults in industrial applications.

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Sixteen years of sound and music computing: A look into the history and trends of the conference and community 17

Sixteen years of sound and music computing: A look into the ...

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17th Sound and Music Computing Conference, SMC 2020

作者： Mauro, Davide Andrea Avanzini, Federico Baraté, Adriano Ludovico, Luca Andrea Ntalampiras, Stavros Dimitrov, Smilen Serafin, Stefania Dept. of Computer & Information Technology Marshall University United States Dept. of Computer Science University of Milano Italy Dept. of Architecture Design and Media Technology Aalborg University Copenhagen Denmark

ISBN: (纸本)9788894541502

This contribution provides an overview of the Sound and Music Computing conference and community over the course of its sixteen years. As a sequel to a previous corresponding contribution investigating the community ten years ago, here we analyze the proceedings of the past editions, as well as the changes in the organization of the conference itself. The analysis reveals the growth of the SMC community in terms of attendees and countries represented at the conference, highlights the changes in trends and topics, and provides insights on the directions of the conference. A reflection is made with regards to the SMC roadmap originally conceived in 2004. Motivated by similar initiatives in "sister"communities, this resource is made available to the community at http://***/. Copyright © 2020 Davide Andrea Mauro et al.

关键词： computer programming

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kanchi Lohitha Lakshmi S.Shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar Sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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Efficient Concept Drift Handling for Batch Android Malware Detection Models

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SSRN 2023年

作者： Molina-Coronado, Borja Mori, Usue Mendiburu, Alexander Miguel-Alonso, Jose Dept. of Computer Architecture and Technology University of the Basque Country UPV/EHU Ps. Manuel Lardizabal 1 Donostia-San Sebastián Gipuzkoa20018 Spain Dept. of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Ps. Manuel Lardizabal 1 Donostia-San Sebastián Gipuzkoa20018 Spain

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data with a more advanced method that selects those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample buffer management mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments. © 2023, The Authors. All rights reserved.

关键词： Mobile security

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Efficient Concept Drift Handling for Batch Android Malware Detection Models

arXiv

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arXiv 2023年

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments. © 2023, CC BY.

关键词： Mobile security

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Intermittent Fault Diagnosis of Analog Circuit Based on Enhanced One-Dimensional Vision Transformer and Transfer Learning Strategy

SSRN

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SSRN 2023年

作者： Wang, Shengdong Liu, Zhenbao Jia, Zhen Vong, Chi-Man Li, Zihao School of Civil Aviation Northwestern Polytechnical University Xi'An710072 China School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Xi'An710055 China Dept. of Computer and Information Science University of Macau 999078 China

Intermittent faults of analog circuits are major causes of built-in test (BIT) false alarms. To enhance the reliability and maintainability of electronic system, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is constructed to realize intelligent diagnosis of intermittent faults. One multiscale convolution fusion module (MSC) is introduced into 1DViT to mine fault features on multiple time scales to enhance the interaction between global and local information. In addition, due to the complex operation and measurement conditions, it is difficult and time-consuming to obtain a large amount of data from practical test. To cope with this problem, transfer learning is introduced. The model will be first pre-trained with adequate simulation data which is readily accessible, and then fine-tuned with actual test data to match the practical distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test. © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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