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检索条件"主题词=software algorithms"

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Resource Optimization Tool

Resource Optimization Tool

引用

IEEE Symposium on Systems and Information Engineering Design, SIEDS

作者： Kelli Carpenter James Williams Nikolas Rocha Sophia Margarella Grado Department of Industrial and Systems Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

Logistics Management Institute (LMI) is a consulting firm that innovates technological solutions for its clients spanning across the defense, space, and healthcare industries. The company is interested in expanding their workforce analytics toolset by digitizing the project assignment process. Current processes are vulnerable to inaccurate estimates of employee skills, project requirements, and project priority. Manually crafting assignments from complex inputs can result in schedules with unequal employee utilization, long project time horizons, and suboptimal project sequencing. LMI has digital tools that quantify employee skills and project requirements and now requires a tool to produce an optimized project schedule. The team developed several mathematical models that are optimized using different solvers, heuristics, and software that converts the mathematical model output into human-readable schedules and performance metrics. This project has provided LMI with a product they can utilize internally or sell to customers to efficiently allocate resources. The optimized assignment approach produces solutions that show several improvements compared to an algorithmic approximation of the current state, with increases in average employee utilization of up to 41% and decreases in makespan of up to 60%.

关键词： Measurement Industries Schedules Sequential analysis software algorithms Medical services Mathematical models

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Experimental Implementation of an OTFS-Based ISAC System

Experimental Implementation of an OTFS-Based ISAC System

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Yuye Shi Buyi Li Xinyuan Wei Hongjia Huang Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Orthogonal Time Frequency Space (OTFS) possesses unique advantages in high-mobility communication scenar-ios due to its robustness against high Doppler frequency shifts, making it an ideal modulation technique for Integrated Sensing and Communication (ISAC) to achieve target sensing functionality. Current research on OTFS for target localization is primarily based on simulations, and the advantages of OTFS signals in target localization have not been fully exploited. Therefore, this paper proposes a three-ellipse positioning algorithm, combined with an off-grid channel estimation method, to achieve the OTFS signal target positioning function. We use the embedded pilot-assisted channel estimation scheme of OTFS to insert pilots, data symbols, and protection areas in the delayed-Doppler plane. Then, the delay and Doppler information of the channel are estimated by the pilot position in the received signal to obtain the physical properties of the channel, including distance, velocity, etc. The three-ellipse positioning algorithm is used to estimate the target position based on these channel parameters. Finally, we build a real communication scenario and our algorithm was experimentally verified by using a USRP-based software defined radio (SDR) platform, proving the effectiveness of our algorithm on the hardware platform.

关键词： Location awareness Time-frequency analysis software algorithms Channel estimation Symbols Integrated sensing and communication Hardware Robustness Doppler effect software radio

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Evaluation of Machine/Deep Learning-Based Methods in Diagnosing Lung Diseases Using Radiographic Images

Evaluation of Machine/Deep Learning-Based Methods in Diagnos...

引用

International Conference on Web Research (ICWR)

作者： Akram Soltanabadi Abdolah Chalechale Dept. of Computer Eng. and Info. Tech. Razi University Kermanshah Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

Lung diseases are one of the most important areas in medicine that significantly impact individuals' quality of life. Accurate diagnosis and appropriate timing of these diseases are crucial. In recent years, with the advancement of technology and the utilization of deep learning methods and neural networks, the diagnosis of lung diseases is performed relatively quickly and accurately. Utilizing the best technologies and algorithms can be effective in diagnosis and treatment of lung diseases. Therefore, investigating, comparing, and selecting the best algorithms for the development and advancement of future work is essential. Advanced technologies in online software and web-based platforms, along with medical knowledge, play a significant role in enhancing the diagnosis and treatment of lung diseases. This process not only aids lung health but also plays a fundamental role in prevention and treatment. This study introduces prominent and new architectures of machine and deep learning-based in lung disease diagnosis using radiographic images. It also examines the role of these architectures in increasing the accuracy, speed of lung disease diagnosis and presents advanced solutions for improving the process of lung disease diagnosis on the web. The obtained results suggest that deep learning methods perform significantly better than traditional feature-based classifiers.

关键词： Deep learning Pulmonary diseases software algorithms Lung Computer architecture software Timing

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PS6D: Point Cloud Based Symmetry-Aware 6D Object Pose Estimation in Robot Bin-Picking

PS6D: Point Cloud Based Symmetry-Aware 6D Object Pose Estima...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yifan Yang Zhihao Cui Qianyi Zhang Jingtai Liu Institute of Robotics and Automatic Information System Nankai University Tianjin China Deep Learning Group Mech-Mind Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

6D object pose estimation holds essential roles in various fields, particularly in the grasping of industrial workpieces. Given challenges like rust, high reflectivity, and absent textures, this paper introduces a point cloud based pose estimation framework (PS6D). PS6D centers on slender and multi-symmetric objects. It extracts multi-scale features through an attention-guided feature extraction module, designs a symmetry-aware rotation loss and a center distance sensitive translation loss to regress the pose of each point to the centroid of the instance, and then uses a two-stage clustering method to complete instance segmentation and pose estimation. Objects from the Siléane and IPA datasets and typical workpieces from industrial practice are used to generate data and evaluate the algorithm. In comparison to the state-of-the-art approach, PS6D demonstrates an 11.5% improvement in ${{\text{F}}_{{1_{inst}}}}$ and a 14.8% improvement in Recall. The main part of PS6D has been deployed to the software of Mech-Mind, and achieves a 91.7% success rate in bin-picking experiments, marking its application in industrial pose estimation tasks.

关键词： Point cloud compression Instance segmentation Accuracy Translation Pose estimation software algorithms Grasping Feature extraction software Surface texture

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Testing Cellular Vehicle-to-Everything Communication Performance and Feasibility in Automated Vehicles *

Testing Cellular Vehicle-to-Everything Communication Perform...

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IEEE Symposium on Intelligent Vehicle

作者： Zhaohui Liang Jihun Han Xiaopeng Li Dominik Karbowski Chengyuan Ma Aymeric Rousseau Civil & Environmental Engineering Department University of Wisconsin Madison Madison WI USA Transportation and Power Systems (TAPS) Division The Argonne National Laboratory Lemont IL USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Many studies have demonstrated the eco-driving capabilities of connected and automated vehicles (CAVs) to significantly enhance mobility systems. The majority of these studies have been conducted using simulations, which fail to capture the effects of practical uncertainties encountered in vehicle-to-anything (V2X) communications. In this paper, we investigated the performance of current cellular V2X (C-V2X) communications through systematic testing and provided a quantitative analysis of key performance indices (e.g., inter-packet gap and packet error rate) across various test scenarios. As one use case to demonstrate the benefits of C-V2X communication on the road, we tested the feasibility of eco-driving for a SAE level 3 (L3) automated vehicle (AV) communicating with a connected urban corridor capable of transmitting traffic light information (i.e., signal phase and timing). To achieve this, we implemented the eco-speed planning algorithm at a high-level in the AV control software system and ensured its interactions with other existing low-level control algorithms, as well as the C-V2X onboard unit. Finally, we experimentally demonstrated eco-driving of the L3 CAV on a scaled-down corridor with two signal-controlled intersections, revealing the AV’s ability to maintain smoother trajectories and avoid unnecessary stops compared to human-driven vehicles.

关键词： Wireless communication Uncertainty Systematics Statistical analysis software algorithms Data models Trajectory

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Two-Stage Transpotation Problem with Unknown Consumer Demands: Consistency Aspects

Two-Stage Transpotation Problem with Unknown Consumer Demand...

引用

Problems of Logistics, Management and Operation in the East-West Transport Corridor (PLMO), International Conference on

作者： Viktor Stovba Olha Khomiak Department of Nonsmooth Optimization Methods V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine Department of Mathematical Methods of Reliability Theory of Complex Systems V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine

ISBN: (数字)9798350353099

ISBN: (纸本)9798350353105

A mathematical model of a two-stage transportation problem with unknown consumer demands and their lower and upper bounds is studied. The necessary and sufficient conditions for consistency of the constraints system of the linear programming problem, which corresponds to this model, are formulated, and justified. The classical two-stage transportation problem is given, to which the above problem is transformed if consumer demands are known, as well as the consistency conditions for constraints system of the problem. Modification of the initial model that permits product transportation only to the fixed number of consumers, which is less than their total number, is given.

关键词： Sufficient conditions Upper bound software algorithms Transportation Programming Linear programming Mathematical models software Partitioning algorithms Logistics

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Establish an Innovative ML-enabled Human Activity Recognition through Edge Computing

Establish an Innovative ML-enabled Human Activity Recognitio...

引用

IEEE Punecon

作者： Ashwini Rajurkar Nilofer Kittad Sulakshana Nagpurkar Department of Computer Engineering Cummins College of Engineering for Women Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

The use of a computer system to recognize human activity or gestures, Human Activity Recognition (HAR), enables real-time behaviour and emotion prediction in individuals. In the fields of ubiquitous edge computing (EC), human behaviour analysis, and human-computer interaction, HAR is a crucial area of research. Machine learning algorithms are used in various fields of research to identify both basic and complicated human activities. In this study, we proposed a novel Garra rufa fish-optimized multi-kernelized support vector machine (GRFO-MSVM) for the recognition of human activity. The suggested approach utilizes the EC’s capabilities to train a model, which is then applied to less potent edge devices for recognition. In this study, we collected a WISDM dataset for human activity. The pre-processed data are extracted using linear discriminate analysis (LDA) to reduce dimensionality in sensor data. The proposed method is implemented using Python software. The proposed method is compared to other existing algorithms. The study indicates the SVM can improve accuracy while performing HAR within the edge. The experimental result shows the SVM achieved better performance in accuracy (98.73%), precision (98.71%), recall (98.72%), and F1-score (98.8%).

关键词： Support vector machines Accuracy Machine learning algorithms Scalability software algorithms Real-time systems software Human activity recognition Edge computing Python

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Exploring the Effectiveness of Different MPPT algorithms for Thermoelectric Generator Optimization: A Comparative Study

Exploring the Effectiveness of Different MPPT Algorithms for...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rajdeep Bhattacharjee Saiful Islam Anindita Deb Nabanita Adhikary Electrical Engineering Department National Institute of Technology Silchar Electrical Engineering Department National Institute of Technology Agartala

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Thermoelectric Generator (TEG) is an essential semiconductor gadget that give rise to electrical current for voltage devices by converting heat flow and the hotness difference between two edges of the Thermoelectric Generator device. Conventionally, a Thermoelectric Generator is useful for small power utilization. When the electrical load matches with the inner resistance of the Thermoelectric Generator, it generates topmost power. However, coordination with impedance cannot be sure in every circumstance. As a consequence, Thermoelectric Generator (TEG) serve at lesser efficiency. Due to these consequences, different maximum power point tracking (MPPT) algorithms are practiced. In this suggested task we modeled the Thermoelectric Generator (TEG) system and simulated the system with two different MPPT algorithms. This task mainly highlights the making use of waste heat generated from various spots like automotive exhaust, bakehouse, kitchen oven, pastry shop, hot water pipes, solar photovoltaic (PV) panels, body heats, etc. which commonly is not taken into deliberation. The proposed model is combined with three different parts of constituent particularly the thermoelectric Generator (TEG), charge controller and load. The entire specimen has been authenticated through MATLAB Simulinkbased software results. The topmost power from the TEG is acquired actively by perturb and observe ( P&O) and variable fractional order Fuzzy logic (VFOLC) maximum power point tracking (MPPT) algorithms.

关键词： Maximum power point trackers software algorithms Water heating Generators Mathematical models software Waste heat

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ZipChannel: Cache Side-Channel Vulnerabilities in Compression algorithms

ZipChannel: Cache Side-Channel Vulnerabilities in Compressio...

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International Conference on Dependable Systems and Networks (DSN)

作者： Marina Minkin Baris Kasikci University of Michigan University of Washington

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

While cache side-channel attacks have been known for over a decade, attacks and defenses have been mostly limited to cryptographic algorithms. In this work, we analyze the security of compression algorithms and their susceptibility to cache side-channels. We design TaintChannel, a tool that automatically detects cache side-channel vulnerabilities and apply the tool to compression software to conduct a study of vulnerabilities in popular compression algorithms—LZ77, LZ78, BWT—and their mainstream implementations. We discover that the implementation of all of these algorithms leak some or all of their input data via cache side-channels. This is concerning, as compression algorithms are widely used in software that operates on sensitive data (e.g., HTTPS). We demonstrate the practicality of these vulnerabilities via two end-to-end attacks on Bzip2. These attacks work in two different threat models and use different attack techniques. Our first attack targets compression within an SGX enclave using the Prime+Probe cache attack technique and extracts the entire input while it is being compressed with an accuracy greater than 99%. Because existing cache attack techniques fall short in targeting applications with larger memory footprint such as compression software, we develop new attack techniques for larger buffers. Our second attack works in the threat model when one application attacks a different application. It allows the attacker to identify which file is being compressed out of multiple options.

关键词： Threat modeling Accuracy software algorithms Side-channel attacks software Compression algorithms

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Advancing Forensic Examination of Cyber Predator Communication Through Machine Learning

Advancing Forensic Examination of Cyber Predator Communicati...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Ayodeji Ogundiran Hongmei Chi Jie Yan Ruth Agada Department of Computer Science Bowie State University Bowie Maryland USA Department of Computer & Information Science Florida A&M University Tallahassee Florida USA

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

By analyzing textual and behavioral patterns in online conversations, machine learning algorithms can assist forensic investigators in identifying potential predators, understanding their strategies, and ultimately enhancing the protection of vulnerable individuals. The importance of digital evidence cannot be overstated because digital evidence is often the key to proving the guilt or innocence of someone for actions for which they have been charged when accused of committing a digital crime. The prevalence of criminal activity on social media sites is often very difficult to determine since there are currently no comprehensive statistics on social media crimes. This increased rate of social media crimes has led to an increasing need for social network forensics. Current digital forensic techniques and tools can also not detect patterns efficiently among large datasets from social media and other online digital domains. Artificial intelligence and natural language processing have been seen as solutions to these problems encountered by forensics experts. The research focuses on the development of a robust machine-learning model capable of distinguishing patterns among suspected predatory conversations, thereby offering a proactive approach to combating online sexual exploitation. This study contributes to the ongoing efforts to create safer digital spaces by unveiling the hidden truths embedded in textual content. The findings hold significant implications for developing effective preventive measures, fostering a more secure online environment, and safeguarding individuals from potential harm.

关键词： Machine learning algorithms Social networking (online) software algorithms Oral communication Machine learning software quality Natural language processing software reliability Security Protection

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