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Clopper-Pearson algorithms for Efficient Statistical Model Checking Estimation

IEEE TRANSACTIONS ON software ENGINEERING 2024年第7期50卷 1726-1746页

作者： Bu, Hao Sun, Meng Peking Univ Sch Math Sci Beijing 100871 Peoples R China

Statistical model checking (SMC) is a simulation-based formal verification technique to deal with the scalability problem faced by traditional model checking. The main workflow of SMC is to perform iterative simulations. The number of simulations depends on users' requirement for the verification results, which can be very large if users require a high level of confidence and precision. Therefore, how to perform as fewer simulations as possible while achieving the same level of confidence and precision is one of the core problems of SMC. In this paper, we consider the estimation problem of SMC. Most existing statistical model checkers use the Okamoto bound to decide the simulation number. Although the Okamoto bound is sound, it is well known to be overly conservative. The simulation number decided by the Okamoto bound is usually much higher than it actually needs, which leads to a waste of time and computation resources. To tackle this problem, we propose an efficient, sound and lightweight estimation algorithm using the Clopper-Pearson confidence interval. We perform comprehensive numerical experiments and case studies to evaluate the performance of our algorithm, and the results show that our algorithm uses 40%-60% fewer simulations than the Okamoto bound. Our algorithm can be directly integrated into existing model checkers to reduce the verification time of SMC estimation problems.

关键词： Estimation Model checking software algorithms Testing Numerical models Costs Computational modeling Statistical model checking formal methods quantitative verification

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Prediction of Continuous Joint Angles of the Lower Limb Based on sEMG by Using the ISSA-HKELM Algorithm

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IEEE SENSORS JOURNAL 2024年第20期24卷 32596-32606页

作者： Tu, Liangjie Fan, Bingfei Du, Mingyu Bao, Guanjun Lv, Bowen Mao, Wenjie Liu, Tao Cai, Shibo Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Zhejiang Univ Technol Coll Mech Engn Minist Educ & Zhejiang Prov Key Lab Special Purpose Equipment & Adv Proc Techn Hangzhou 310014 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

Continuous prediction of limb joint angles is a vital task in exoskeleton robot motion control. This article introduces a method for continuous prediction of hip and knee joint angles based on a wearable sEMG signal acquisition system and the improved sparrow search algorithm (ISSA)-hybrid kernel extreme learning machine (HKELM) prediction model. We first developed a novel wearable eight-channel lower limb sEMG signal collection system and then proposed an ISSA-HKELM prediction model, which is an HKELM with autonomous hyper-parameter optimization using an ISSA. Using this model, we established a mapping relationship between sEMG signals and joint angles. The input of the model is the eight-channel sEMG signal at the current moment, and the output is the hip and knee angles at specified time intervals in the future. We recruited eight healthy participants for the validation experiments. Results indicate that the developed wearable sEMG signal acquisition system and the proposed ISSA-HKELM model can achieve continuous prediction of hip and knee joint angles up to 80 ms in advance. The average RMSE for hip and knee joint angles are 4.61 degrees and 5.95 degrees, with average R-2 of 0.902 and 0.848, respectively, demonstrating a high level of fitting. Therefore, this method effectively enables advanced prediction of limb movement and significantly enhances real-time responsiveness and adaptability in human-robot interaction motion control.

关键词： Sensors Predictive models Kernel Muscles software algorithms Real-time systems Trajectory Hybrid kernel extreme learning machine (HKELM) improved sparrow search algorithm (ISSA) joint angle prediction wearable sEMG signal acquisition system

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Energy Management of Home Devices With Smart Response for the Energy Generation Profile

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 6995-7007页

作者： Powroznik, Piotr Szczesniak, Pawel Univ Zielona Gora Inst Metrol Elect & Comp Sci PL-65516 Zielona Gora Poland Univ Zielona Gora Inst Automat Control Elect & Elect Engn PL-65516 Zielona Gora Poland

In modern power systems, a large supply of energy from renewable energy sources is often not synchronized with energy consumption. In order to ensure energy balance, it is necessary to increase its consumption during large generation from renewable sources. For this purpose, a digital algorithm for elastic energy management was developed. This article indicates the main assumptions of the algorithm and its hardware implementation. The JSON format was proposed for exchanging data between the hardware and software part of the system. The results of the presented simulation and experimental studies confirmed the effectiveness of the proposed algorithm. A statistical approach was used in the simulation studies employing smart appliances in 1000 households. The presented results indicate that the implementation of digital energy management algorithms in local balancing areas employing smart home devices connected via communication interfaces will increase the potential for the use of electricity generated by renewable sources.

关键词： Energy management Power system stability Task analysis Renewable energy sources Industries Energy consumption software algorithms Elastic energy management (EEM) algorithm embedded systems energy demand control greedy randomized adaptive search procedure (GRASP) algorithm heuristic algorithm Internet of Things (IoT) NP problem renewable energy sources (RES) smart appliances (SAs)

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Modeling and Analysis of the LatestTime Message Synchronization Policy in ROS

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第11期43卷 3576-3587页

作者： Wu, Chenhao Li, Ruoxiang Zhan, Naijun Guan, Nan Chinese Acad Sci Inst Software SKLCS Beijing 100045 Peoples R China Univ CAS Sch Comp Sci Beijing 100049 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China

Sensor fusion plays a critical role in modern robotics and autonomous systems. In reality, the sensor data destined for the fusion algorithm may have substantially different sampling times. Without proper management, this could lead to poor sensor fusion quality. Robot operating system (ROS) is the most popular robotic software framework, providing essential mechanisms for synchronizing messages to mitigate timing inconsistencies during sensor fusion. Recently, ROS introduced a new LatestTime message synchronization policy. In this article, we formally model the behavior of the LatestTime policy and analyze its worst-case real-time performance. Our investigation uncovers a defect of the LatestTime policy that may cause infinite latency in publishing subsequent outputs. We propose a solution to address this defect and develop safe and tight upper bounds on worst-case real-time performance, in terms of both the maximal temporal inconsistency of its outputs and the incurred latency. Experiments are conducted to evaluate the precision, safety and robustness of our theoretical results.

关键词： Analytical models Upper bound Publishing software algorithms Sensor fusion Robot sensing systems Real-time systems software Safety Synchronization Autonomous driving message synchronization ROS sensor fusion

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The Messy Reality Behind a Silicon Valley Unicorn

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IEEE SPECTRUM 2024年第4期61卷 44-49页

作者： Shestakofsky, Benjamin

A VC-backed startup 's push for growth left little time for actual engineering

关键词： software Task analysis Companies Venture capital software algorithms Interviews Explosives

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A Fast Clustering Algorithm for Modularization of Large-Scale software Systems

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IEEE TRANSACTIONS ON software ENGINEERING 2022年第4期48卷 1451-1462页

作者： Teymourian, Navid Izadkhah, Habib Isazadeh, Ayaz Univ Tabriz Dept Comp Sci Tabriz *** Iran

A software system evolves over time in order to meet the needs of users. Understanding a program is the most important step to apply new requirements. Clustering techniques through dividing a program into small and meaningful parts make it possible to understand the program. In general, clustering algorithms are classified into two categories: hierarchical and non-hierarchical algorithms (such as search-based approaches). While clustering problems generally tend to be NP-hard, search-based algorithms produce acceptable clustering and have time and space constraints and hence they are inefficient in large-scale software systems. Most algorithms which currently used in software clustering fields do not scale well when applied to large and very large applications. In this paper, we present a new and fast clustering algorithm, FCA, that can overcome space and time constraints of existing algorithms by performing operations on the dependency matrix and extracting other matrices based on a set of features. The experimental results on ten small-sized applications, ten folders with different functionalities from Mozilla Firefox, a large-sized application (namely ITK), and a very large-sized application (namely Chromium) demonstrate that the proposed algorithm achieves higher quality modularization compared with hierarchical algorithms. It can also compete with search-based algorithms and a clustering algorithm based on subsystem patterns. But the running time of the proposed algorithm is much shorter than that of the hierarchical and non-hierarchical algorithms. The source code of the proposed algorithm can be accessed at https://***/softwareMaintenanceLab.

关键词： Clustering algorithms software algorithms Search problems software systems Semantics software architecture software clustering software modularization software maintenance software comprehension architecture recovery

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PyExaFMM: An Exercise in Designing High-Performance software With Python and Numba

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COMPUTING IN SCIENCE & ENGINEERING 2022年第5期24卷 77-84页

作者： Kailasa, Srinath A. Wang, Tingyu Barba, Lorena Betcke, Timo UCL Math London WC1E 6BT England George Washington Univ Washington DC 20052 USA George Washington Univ Mech & Aerosp Engn Sch Engn & Appl Sci Washington DC 20052 USA UCL Computat Math Dept Math London WC1E 6BT England

Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter, and that fully utilizes the resources of modern CPUs. This means support for parallel multithreading and auto-vectorization if available, as with compiled languages such as C++ or Fortran. In this article, we document our experience developing PyExaFMM, a multithreaded Numba implementation of the fast multipole method, an algorithm with a nonlinear data structure and a large amount of data organization. We find that designing performant Numba code for complex algorithms can be as challenging as writing in a compiled language.

关键词： Codes Scientific computing High performance computing software algorithms Ecosystems Organizations Writing

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Ultrafast Ultrasound Imaging System With Beamforming on Single FPGA

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Xi Qi, Sheng Li, Yifan Xu, Lijun Ma, Jianguo Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Shandong Jianzhu Univ Sch Sci Jinan 250100 Peoples R China

Ultrafast ultrasound imaging technologies, such as ultrasound localization microscopy (ULM) and functional ultrasound (FUS), are gaining significant attention in the ultrasound field, driving the demand for higher frame rates in open ultrasound systems. However, current open ultrasound systems face the challenge of balancing high imaging frame rates with the complexity of hardware architecture. This work reports the detailed implementation and hardware architecture of a 128-channel ultrafast ultrasound platform based on a single field-programmable gate array (FPGA). This platform connects the ultrasound card to a personal computer (PC) via peripheral component interconnect express (PCIe) 3.0, providing excellent compatibility. The platform uses custom instructions to implement flexible parameter configuration in the FPGA. It can control the transmission and reception of signals, the output of raw or beamformed RF data, and the sampling depth. Additionally, a 64 sub-aperture delay-and-sum (DAS) beamformer within the FPGA is implemented in the platform. This beamformer gives output as a 128x2048 image at 10000 frames/s (fps) using plane wave. The core components of the platform measure only 12x21 cm, achieving a compact design while delivering high-frame-rate (HFR) imaging.

关键词： Ultrasonic imaging Field programmable gate arrays Delays Array signal processing Imaging Hardware Computer architecture software Radio frequency software algorithms Beamforming field-programmable gate array (FPGA) ultrasound system ultrafast ultrasound

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Toward Improving the Efficiency of software Development Effort Estimation via Clustering Analysis

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IEEE ACCESS 2022年 10卷 83249-83264页

作者： Vo Van Hai Ho Le Thi Kim Nhung Prokopova, Zdenka Silhavy, Radek Silhavy, Petr Tomas Bata Univ Zlin Fac Appl Informat Zlin 75501 Czech Republic

Introduction: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group's Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sec

关键词： Estimation software Clustering methods software algorithms Organizations Industries Computational modeling software effort estimation function point analysis dataset clustering K-means categorical variables machine learning

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Pipelining of a Mobile SoC and an External NPU for Accelerating CNN Inference

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IEEE EMBEDDED SYSTEMS LETTERS 2024年第2期16卷 150-153页

作者： Kwon, Jinse Lee, Jemin Kim, Hyungshin Chungnam Natl Univ Dept Comp Sci & Engn Daejeon 34134 South Korea Univ Sci & Technol Dept Artificial Intelligence Daejeon 34113 South Korea Elect & Telecommun Res Inst Artificial Intelligence Comp Res Lab Daejeon 34129 South Korea

Convolutional neural networks (CNNs) algorithms are increasingly being deployed on edge devices with the co-growth of hardware and software. Deploying CNNs on resource-constrained devices often requires optimization of CPUs and GPUs. While a dedicated hardware, such as a neural processing unit (NPU), has been successfully introduced, cooperative methods between CPU, GPU, and NPU are still immature. In this letter, we propose two approaches to optimize the integration of a mobile system-on-chip (SoC) with an external NPU (eNPU) to achieve harmonious pipelining and enhance inference speed and throughput. The first approach involves a basic linear algebra subprogram library search scheme to allocate optimal libraries per layer on the host side, while the second approach optimizes performance by searching for model slice points. We utilize CPU-based NNPACK, OpenBLAS, and GPU-based CLBlast as computing libraries that are automatically allocated. The entire neural network (NN) is optimally split into two segments based on the characteristics of the NN layers and hardware performance. We evaluated our algorithm on various mobile devices, including the Hikey-970, Hikey-960, and Firefly-rk3399. Through experiments, we show that the proposed pipeline inference method reduces latency by 10% and increases throughput by more than 17% compared to parallel execution on an eNPU and SoC.

关键词： Performance evaluation software algorithms Throughput Libraries Hardware software System-on-chip Convolutional neural network (CNN) inference pipelining model slicing neural processing unit (NPU) pipelining

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