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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

引用

IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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User Interface Design’s Impact on Customer Satisfaction and Loyalty in SaaS E-Commerce

User Interface Design’s Impact on Customer Satisfaction and...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Eka Purnama Harahap Pandu Hermawan Dhiyah Ayu Rini Kusumawardhani Nina Rahayu Maulana Arif Komara Harry Agustian Master of Technology Information University of Raharja Tangerang Indonesia Dept. of Information System Bina Bangsa University Serang Indonesia Dept. of Information System Pandawan Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems University of Raharja Tangerang Indonesia Dept. of Digital Bussiness CAI Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems Sesindo Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This study aims to analyze the impact of user interface (UI) design on customer satisfaction and loyalty in SaaS e-commerce applications, using a quantitative approach to measure the perceptions of SaaS e-commerce application users and determine how visual aspects and interface functionality contribute to the user experience, with data analysis conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, indicating that an intuitive and responsive interface design significantly influences increased customer satisfaction, which in turn strengthens their loyalty to the application; the study seeks to provide insights that developing an effective user interface is crucial for building long-term relationships with customers and reaffirms the importance of integrating good interface design in e-commerce application development strategies, and further suggests that incorporating user-centered design principles and continuously collecting user feedback can enhance the user experience, thereby allowing developers to create interfaces that not only attract users but also retain them, essential for e-commerce businesses aiming to improve user engagement and loyalty in a highly competitive market.

关键词： Customer satisfaction Buildings Psychology Virtual reality User interfaces User experience Mathematical models Electronic commerce Usability Business

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition

BioPoint: Enhancing Human-Computer Interaction through Singl...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xavier Isabel Evan Campbell Loïc Olivier Gabriel Gagné Erik Scheme Ulysse Côté-Allard Gabriel Gagnon-Turcotte Dept. of Electrical and Computer Engineering Laval University Québec Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton Canada Dept. of Technology Systems University of Oslo Oslo Norway

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero." Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process’s appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG’s potential for human-computer interaction.

关键词： Human computer interaction Wrist Accuracy Pipelines Gesture recognition Rhythm Electromyography Recording Synchronization Testing

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Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol 23

Data Security Enhancement in Cloud Computing by Proposing A ...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Sahar Ebadinezhad Halmat Ayub Abdulmajed Department of Computer Information Systems Near East University North Cyprus and Computer Information Systems Research and Technology Center (CISRT) Near East University North Cyprus Dept. Engineering and Computer Systems Lebanese French University (LFU) Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.

关键词： Cloud Services Cryptography Data Integrity Data Security Dual Key Encryption

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images 3

A Deep Neural Network for Detecting Broken and Scattered Pow...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Pei, Qiugen Lai, Yuxuan Wei, Nianxing Wu, Shiting Guangdong Power Grid Co. Ltd. Enterprise Architecture and Digitalization Department Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Guangdong Power Grid Co. Ltd. Huizhou Boluo Power Supply Bureau Huizhou China

ISBN: (纸本)9798350365443

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the 'EWF _detection' (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Aerial photography

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI 15

Analysis of Defect Associated with Powder Bed Fusion with De...

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15th International Conference on Knowledge and Smart technology, KST 2023

作者： Pratap, Ayush Sardana, Neha Utomo, Sapdo John, A. Karthikeyan, P. Hsiung, Pao-Ann Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665477123

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0. © 2023 IEEE.

关键词： computer vision

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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TrustEvent: Cross-Platform IoT Trigger Event Verification Using Edge Computing

TrustEvent: Cross-Platform IoT Trigger Event Verification Us...

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IEEE International Conference on Communications (ICC)

作者： Trent Reichenbach Chenglong Fu Xiaojiang Du Jia Di Yuede Ji Dept. of Electrical and Computer Engr Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems UNC Charlotte Charlotte NC USA Dept. of Computer Science and Engr University of Arkansas Fayetteville AR USA Dept. Computer Science and Engr University of North Texas Denton TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As smart home IoT systems gain popularity, they inevitably become targets for security risks and concerns. Among various cyber-attacks targeting these systems, the fake event attack poses significant issues due to its ability to manipulate secure devices through automation rules. In response to this threat, we propose TrustEvent - a system designed to offer end-to-end event signature verification. By integrating TrustEvent with existing home automation platforms, event authenticity is verified against signatures generated from edge devices before these events trigger automation rule execution. Notably, we have developed a signature proxy module, enhancing our system's compatibility across various platform scenarios. We have implemented a TrustEvent prototype in conjunction with existing commercial smart home IoT platforms, evaluating its overhead in the process. Our experimentation demonstrates that our system only marginally increases the automation execution latency, by an average of 3.74 seconds, representing a acceptable compromise for enhanced security.

关键词： Handwriting recognition Automation System performance Instruments Prototypes Smart homes Static analysis

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Secure and Efficient Key Management for Deduplicated Cloud Storage systems

Secure and Efficient Key Management for Deduplicated Cloud S...

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Security of Information and Networks (SIN), International Conference on

作者： Jay Dave Anshul Kanodia Raj Srivastava Kushagra Singh Prithvi Hegde Hitaishi Desai Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India

Deduplication is a ubiquitous compression technique for cloud storage servers. It reduces storage and bandwidth requirements by avoiding duplicate copies of data. However, developing an encryption scheme for deduplication is a critical challenge because traditional encryption techniques are not compatible with deduplication. The existing state-of-the-art encryption schemes are at risk of brute-force attacks as they use deterministic techniques for key generation. Moreover, the user in the recent schemes suffers from storage overhead of deduplication metadata and encryption keys. In this paper, we introduce a secure and efficient key management scheme for deduplication. In our scheme, the user divides the file into blocks and encrypts each block using a random key. As a result, our scheme protects against brute-force attacks. In addition, the user securely shares the encryption keys on public storage servers with the aid of a private server. As a result, the proposed approach causes lower storage overhead. We implement our scheme in a real cloud scenario and evaluate the performance of our approach in terms of storage and computation overhead.

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