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IEEE international Workshop on Robot and Human Communication (ROMAN)

作者： Rabeya Jamshad Katelyn E. Fry Yu-ping Chen Ayanna Howard School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology Atlanta GA USA Department of Physical Therapy Georgia State University Atlanta GA USA School of Interactive Computing and the School of the Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

According to data from the Centers for Disease Control and Prevention, developmental disorders such as Autism Spectrum Disorder (ASD) and Cerebral Palsy (CP) affect nearly one in six children between the ages of 3 to 17 in the United States alone. In order to improve the quality of life for these individuals, there is increased emphasis on providing early intervention at infancy, when key developmental milestones are being achieved. this however requires accurate early detection of motor development delays in at-risk infants. Our research focuses on enabling early detection through the design of a robotic crib mobile that affects infant behavior. Stimuli integrated into the robotic mobile can be used to encourage certain motions such as kicking among infants in order to study infant motor development and identify at-risk populations. In this paper, we propose the design of such a robotic crib mobile and discuss preliminary results from deploying the mobile in the infants' home environment during a pilot study.

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Improving infrared MSG satellite images classification, Application on rainy areas detection

Improving infrared MSG satellite images classification, Appl...

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international conference on Modelling, Identification and Control

作者： Mohsene Abdelfettah TEBBI Boualem HADDAD Laboratory of image processing and radiation Faculty of Electronics and Computer Science University of sciences and technology Houari Boumediene

ISBN: (纸本)9781509015948

the investigation of the positive impact of using artificial intelligence systems in meteorological satellite images classification is presented in this study. An application for classifying clouds observed northern Algeria by the MSG (Meteosat Second Generation) satellite is developed in this work. the proposed identification system is based on the spectral information derived from the infrared channels of MSG satellite images. the classification aim is to identify pixels as rainy or not, and to delineate precipitating areas for northern Algeria in real time. An SVM (Support Vector Machine) classifier is adopted in this work as the intelligent system used for the identification. Models are trained and validated against in situ rainfall measurements, collected during the rainy period of the year 2012, by automatic rain-gauge stations distributed over the study area (North of Algeria). Five statistical assessors are used to evaluate performances. Results of the classification by the SVM are compared to a statistical method, based on thresholding in histograms representing classes' features. the classification error has been significantly reduced when using the SVM classifier, allowing a more accurate estimation of precipitations by infrared MSG satellite images.

关键词： Meteorological satellite Multispectral MSG images Identification system Classification Support vector machine

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AUTOFRAME - A Software-Driven Integration Framework for Automotive Systems

AUTOFRAME - A Software-Driven Integration Framework for Auto...

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international conference on intelligent Transportation

作者： Sven Kirchner Nils Purschke Chengdong Wu Muhammed Aqib Khan Divye Dixit Alois C. Knoll Technical University of Munich Garching Bayern Germany Indian Institute of Technology Mandi Mandi Himachal Pradesh India

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

the evolution of automotive technologies towards more integrated and sophisticated systems requires a shift from traditional distributed architectures to centralized vehicle architectures. this work presents a novel framework that addresses the increasing complexity of Software Defined Vehicles (SDV) through a centralized approach that optimizes software and hardware integration. Our approach introduces a scalable, modular, and secure automotive deployment framework that leverages a hardware abstraction layer and dynamic software deployment capabilities to meet the growing demands of the industry. the framework supports centralized computing of vehicle functions, making software development more dynamic and easier to update and upgrade. We demonstrate the capabilities of our framework by implementing it in a simulated environment where it effectively handles several automotive operations such as lane detection, motion planning, and vehicle control. Our results highlight the framework's potential to facilitate the development and maintenance of future vehicles, emphasizing its adaptability to different hardware configurations and its readiness for real-world applications. this work lays the foundation for further exploration of robust, scalable, and secure SDV systems, setting a new standard for future automotive architectures.

关键词： Industries Lane detection Computer architecture Aerodynamics Software Hardware Planning Complexity theory Vehicle dynamics Standards

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Demand Response Scheme: Achieving Sustainability in distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

the rapid development of Internet of things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of things Sustainable development Optimization

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Application methods for a niche genetic algorithm for design of reactive distillation processes

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2015年 30卷 313-326页

作者： Matsumoto, Hideyuki Lim, Kai Tun Kuroda, Chiaki Yamaki, Takehiro Matsuda, Keigo Institute of Systems Science National University of Singapore 25 Heng Mui Keng Terrace Singapore119615 Singapore Department of Chemical Engineering Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan Department of Organic Material Engineering Yamagata University 4-3-16 Jonan Yonezawa-shi Yamagata992-8510 Japan Department of Chemistry and Chemical Engineering Yamagata University 4-3-16 Jonan Yonezawa-shi Yamagata992-8510 Japan

ISBN: (纸本)9783319135441

A purpose of this chapter is to investigate application methods for a niche GA to design of Reactive Distillation (RD) process involving the production and separation of ethyl acetate. First, optimization system based on the Multi- Niche Crowding – Genetic Algorithm (MNC-GA) is demonstrated to be effective in searching preferable designs of RD process using multiple feeds from each reactant. It is seen that the MNC-GA allows the search to yield various design solutions without causing remarkable performance degradation for searching the best design. thus, authors investigate application methods for utilizing the various design solutions from the viewpoint of analysis of operability of searched process. It is shown that the multiple preferable designs obtained using the MNC-GA are useful for searching more preferable designs by steady-state process simulation and for sensitivity analysis which provides us with insights to the dynamics in RD column. In addition, intermediate simulation results, which are obtained through all the generations, are demonstrated to produce informative distributed plots for the sensitivity analysis, as compared to the normalGA where the tournament-based selection method is used. © Springer international Publishing Switzerland 2015.

关键词： Genetic algorithms

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TMan: A High-Performance Trajectory Data Management System Based on Key-Value Stores

TMan: A High-Performance Trajectory Data Management System B...

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international conference on Data Engineering

作者： Huajun He Zihang Xu Ruiyuan Li Jie Bao Tianrui Li Yu Zheng School of Computing and Artificial Intelligence Southwest Jiaotong University China JD iCity JD Technology Beijing China JD Intelligent Cities Research China Chongqing University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

the effective management of trajectory data heavily relies on the utilization of fundamental spatio-temporal queries. the surge in trajectory data, with its dynamic spatio-temporal properties, poses notable management challenges. Existing systems are inadequate in providing fine-grained trajectory representations and efficient architecture for processing queries, leading to significant computational overhead. this paper introduces TMan to address these challenges. First, TMan presents two innovative index structures that precisely capture the spatio-temporal characteristics of trajectory data. Compared to the state-of-the-art indexes, our indexes for temporal range and spatial range queries can reduce the number of retrievals by up to 77% and 83%, respectively. Next, TMan devises concise and effective encoding methods for these indexes. Leveraging these indexes, TMan provides a distributed storage structure and an index caching mechanism for efficiently managing trajectories in key-value data stores. Moreover, TMan introduces a parallel query processing approach incorporating a push-down strategy to enhance the efficiency of fundamental queries. Extensive experimental results demonstrate that TMan's index structures and architecture outperform the baselines.

关键词： Shape Query processing distributed databases Computer architecture Data engineering Encoding Trajectory

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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Proceedings of the 2022 11th international conference on computing and Pattern Recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based Approach

Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based...

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international conference on distributed computing Systems

作者： Weizhen Han Enshu Wang Bingyi Liu Zhi Liu Xun Shao Libing Wu Jianping Wang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Sanya Science and Education Innovation Park of Wuhan University of Technology School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Aichi Japan Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

With the capability of intelligent control and communicating with surrounding vehicles and infrastructures, connected and automated vehicles (CAVs) can drive cooperatively and have more positive effects on traffic efficiency. Cooperative and real-time path planning for CAVs stands as a pivotal solution to mitigate traffic congestion and augment travel efficiency. However, most of the existing path planning schemes predominantly concentrate on minimizing the travel times of vehicles, sidelining the broader issue of alleviating traffic congestion in urban settings. therefore, in this paper, we propose a novel collaborative vehicle path planning scheme, leveraging the intelligent control and the communicating ability of CAVs. the primary objective is to reduce traffic congestion within the overall transportation system and improve traffic efficiency. Specifically, we focus on a general urban scenario with various types of vehicles, including CAVs, connected vehicles (CVs), and traditional human-driven vehicles (TVs), To enhance traffic efficiency in such a scenario, we design a collaborative path planning scheme to discover the efficient paths for both CAVs as well as CVs. In this scheme, we treat each CAV as an agent and formulate the multiple CAVs' path-planning problem as a Markov game. To solve the above Markov game, we design a multi-agent convolutional attention reinforcement learning (MACA) framework to generate paths with minimal travel time for CAVs. More concretely, the proposed MACA framework incorporates a convolutional neural network (CNN) layer to capture spatial correlation behind traffic conditions. Additionally, a graph attention network (GAT) layer is employed to integrate the influence of neighboring agents during the path-planning process. To further reduce traffic congestion, we extend the MACA framework into a collaborative MACA (C-MACA) scheme in vehicular networks, where CAVs are empowered to periodically broadcast their path information to surr

关键词： Heuristic algorithms Collaboration Transportation Games Reinforcement learning Path planning Real-time systems

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IDEA: intelligent Diffusion Model for Edge Cache System in Content Delivery Network

IDEA: Intelligent Diffusion Model for Edge Cache System in C...

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Algorithms, computing and Artificial Intelligence (ACAI), international conference on

作者： Yicong Zhao Zhi Li Lingshan Kong Song Guo Longfei Jin Zhao Yang Jing Yu Zichen Wang China Mobile Communications Group Co. Ltd Beijing China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Content Delivery Network is an overlay network, which distributes the content from the origin servers to dis-tributed edge service nodes, enabling users to get the required content from a nearby location, therefore, improving the per-ceived user experience. In order to accelerate content transmission speed and reduce network latency and congestion, CDN load balancing technology was developed. this technology optimizes the allocation of incoming traffic across multiple servers, ensuring equitable workload distribution and mitigating risks of server overload or underutilization. therefore it improves the respon-siveness of the website, reduces communication overhead, and maximizes the efficiency of computational resources. However, the balancing methods proposed by academia use various algorithms or approaches, but these algorithms often fail to account for the trade-off between communication costs and latency especially when the service machine is down. In recent years, most load balance techniques in the industry have primarily used edge node storage resources while neglecting computational resource utilization. To reduce both communication costs in routing and the waiting times of end-users, it is essential to direct user requests to the most optimal servers available. this study presents the implementation and design of IDEA, an intelligent Diffusion Model for Edge Cache System in CDN, which reduces the cost of communication link and the time required to fetch distributed contents by introducing Long-Short Term Memory Network combined with a new meta-heuristic algorithm. We then compare it to other algorithms available in this paper. Experimental results show that this algorithm exceeds the performance of current methods across the key metrics outlined above.

关键词： Costs Content distribution networks Heuristic algorithms Diffusion models Routing User experience Computational efficiency Servers Resource management Artificial intelligence

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Enhancing Dietary Monitoring using Deep Learning: Food Recognition and Calorie Estimation

Enhancing Dietary Monitoring using Deep Learning: Food Recog...

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international conference on computing and Networking technology (ICCNT)

作者： M. Suneela Mandadi Sahithi Nuthalapati Vamsi Nunsavathu Balaji Naik Narra Sujitha Chowdary Dept. of Computer Science & Engineering Vignan’s Lara Institute of Technology & Science College Guntur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the project “Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation” introduces an innovative method aimed at improving dietary tracking and fostering healthier eating habits. By employing cutting-edge deep learning techniques, its core objective is to precisely identify various food items and rapidly estimate their caloric content, offering users advanced and personalized monitoring capabilities. Utilizing the Python programming language alongside the MobileNet architecture model, the project underwent rigorous training and evaluation using the expansive Food 101 dataset, comprising 37,046 food images distributed across 101 distinct classes. Notably, the model demonstrated exceptional performance, achieving a training accuracy of $\mathbf{9 7 . 0 2 \%}$ and a validation accuracy of $\mathbf{9 8 . 1 7 \%}$, highlighting the efficacy of this approach in accurately categorizing a wide array of food items. this system provides users with several crucial functionalities. Furthermore, it furnishes users with essential insights by estimating the caloric content of recognized foods, facilitating effective monitoring of dietary intake. the intelligent diet monitoring capabilities enabled by Food Recognition and Calorie Estimation empower users to make informed choices regarding their dietary preferences. through the continuous tracking and analysis of their daily food consumption, users can glean valuable insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a well-balanced and healthy diet. Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation stands as a prominent illustration of successful implementation of deep learning techniques, particularly with the utilization of the MobileNet architecture, for food recognition and calorie estimation. With its remarkable accuracy, real-time processing capabilities, and intelligent monitoring features, this projec

关键词： Deep learning Training Accuracy Scalability Estimation Computer architecture Real-time systems Object recognition Monitoring Python

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