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Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Shanshan Cao Wei Sun Fantao Kong Jifang Liu Chinese Academy of Agricultural Sciences Agricultural Information Institute Beijing China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

Many existing variogram theoretical models which include nonlinear model, linear model and hole effect model had been existed. However, in the process of estimating the spatial distribution of soil heavy metals, the selection of interpolation method, variogram fitting and parameter setting all depended on human subjective experience, which greatly affects the accuracy and reliability of spatial estimation. Based on the mixed programming of Java and python, this study realized the optimization software tool of variogram model for heavy metals spatial estimation variogram in soil, which integrated DS evidence theory and deep learning algorithm, and realized the optimal selection of six mainstream spatial interpolation algorithms, optimal fitting of variogram and automatic parameter setting. The application had showed that the system is simple and easy to use, which could avoid the interference of human experience in the process of spatial estimation, improves the accuracy and accuracy of spatial estimation, and could provide good tool support for researchers in the field of spatial prediction of soil element content.

关键词： Deep learning Interpolation Accuracy software algorithms Fitting Estimation Metals

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Design of Translation Accuracy Correction Algorithm for English Translation software Based on Deep Learning

Design of Translation Accuracy Correction Algorithm for Engl...

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Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Hao Tang Jianbo Xu Xiangnan University Chenzhou Hunan China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

In today’s information society, how to quickly and efficiently break down language barriers between people through various technological means has become a common problem. Building the corpus required for statistical machine translation is an important step in ensuring the feasibility of this method in practical applications. Based on the initial language knowledge provided by linguists, semi-automatic processing of human-computer interaction is carried out, marking the part of speech, the meaning of each word in each phrase, the syntactic structure and semantic combination relationship between words, and turning it into a familiar corpus. In English translation, due to its own characteristics, people have conducted extensive research and analysis on it. In view of this, this paper makes a detailed study on the development and application of English translation software, analyzes sentences from bottom to top by using the moving-in convention algorithm, and processes tenses. Finally, the translated sentences are connected and the mood is treated accordingly.

关键词： Vocabulary Accuracy software algorithms Education Semantics Syntactics Turning

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Assurance of Machine Learning/TinyML in Safety-Critical Domains

Assurance of Machine Learning/TinyML in Safety-Critical Doma...

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2022 IEEE Symposium on Visual Languages and Human-Centric Computing

作者： Iqbal, Zain Univ Padua Dept Math Padua Italy

ISBN: (数字)9781665442145

ISBN: (纸本)9781665442152;9781665442145

Machine Learning (ML) is being applied in a variety of systems, with outcomes that have been shown to outperform human ability under certain conditions [1] . In sectors such as healthcare [2] , automotive [3] , and manufacturing [4] , systems often feature high degrees of autonomy and safety, which causes them to be categorized as \"safety-critical\" (or, equivalently, \"high assurance\"). The appeal of adopting ML in safety-critical application domains is rising steadily in anticipation of the \"intelligence bonus\" expected of deploying ML-boosted applications operationally. Obviously, this drive has also caused much attention into the question of the level of assurance that can be achieved for such systems after they incorporate such additional components. Ways to reap the anticipated potential of ML and, more generally, of Artificial Intelligence (AI), are being explored also in materials science, robotics, and numerous other engineering systems. Application domains that are rich of field data (as in manufacturing, testing, and service) and that pursue multi-objective optimizations are especially suited for earning boosting from ML/AI data-driven algorithms. For the integration of data intensive ML algorithms, the aerospace industry provides a variety of unique opportunities and challenges. The influence of data science on the aerospace industry will be felt across the whole supply chain and development process, including in testing and assessment [5] . Owing to the computationally and storage intensive nature of current state-of-the-art implementations of ML, all data acquired from the field are transferred to the cloud for processing, which obviously incurs massive latency that renders the objective untenable for latency-sensitive embedded applications. TinyML is an emerging effort that integrates ML into embedded Internet of Things (IoT) devices with the potential to revolutionize aerospace industry and several other application domains with similar needs and

关键词： Visualization Costs Soft sensors software algorithms Supply chains Manufacturing Internet of Things

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LoopIn: A Loop-Based Simulation Sampling Mechanism

LoopIn: A Loop-Based Simulation Sampling Mechanism

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IEEE International Symposium on Performance Analysis of Systems and software (ISPASS)

作者： Vengalam, Uday Kumar Reddy Sharma, Anshujit Huang, Michael Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

ISBN: (纸本)9781665459549

Understanding program behavior is at the heart of general-purpose architecture design. Whether we are testing a new design offline or making a design adapt to changing behavior online, a central assumption is that the test cases represent real workload in steady state. Typical computer programs have been known to exhibit patterns of runtime behavior that repeat during the course of their execution. Simulation and adaptation strategies all exploit this repetition to some extent. In this paper, we introduce a simple mechanism that is more explicit in identifying and exploiting behavior repetition at the granularity of (broadly defined) loops. The result is that a typical benchmark will be categorized into tens of loops. In terms of architectural simulations, this strategy will create a moderate number (on the orders of 100) of relatively short (tens of thousands of instructions) segments. There are two major benefits in our view. The first and more quantifiable benefit is that, the strategy requires less simulation and obtains increased accuracy compared to the commonly used SimPoint approach. Second, instead of depicting average statistics of an entire program, we can accurately describe intra-program behavior variation, which simple sampling strategies cannot. LoopIn produces many small simulation segments. In certain usage scenarios, microarchitectural state warm-up may be costly. In these cases, an existing tool BLRL can help create efficient warm-up arrangements.

关键词： Estimation error Runtime software algorithms Benchmark testing software Behavioral sciences Steady-state

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Design of Full-Drive Multi-finger Dexterous Hand 7

Design of Full-Drive Multi-finger Dexterous Hand

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7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Zhou, Xuanyi Zhu, Yunfei Qian, Shaoming Ge, Han Cai, Shibo Bao, Guanjun Zhejiang Univ Technol Key Lab Special Purpose Equipment & Adv Proc Tech Hangzhou Peoples R China China Acad Space Technol Beijing Inst Spacecraft Environm Engn Beijing 100094 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

With the rapid development of science and technology in recent decades, the multi-finger dexterous hand has the characteristics of good flexibility and strong adaptability. At the same time, the development of service robots has further promoted the multi-finger dexterous hand. The performance of the multi-finger dexterous hand not only considers the ontology structure of the dexterous hand but is also closely related to its control method. Based on the ontology structure of the multi-finger dexterous hand, a transmission structure of the fingers is proposed. The revolute joint is designed for modular fingers. Afterward, the robotic kinematic and control algorithms are analyzed. According to the driving structure of the multi-finger dexterous hand, the hardware and software of the control system is designed. The visualization software of the multi-fingered dexterous hand is developed through simulation and teleoperation. Finally, the effectiveness of the proposed system is verified through the experiments.

关键词： Visualization Mechatronics Service robots software algorithms Kinematics Ontologies Control systems

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EaseMiss: HW/SW Co-Optimization for Efficient Large Matrix-Matrix Multiply Operations 15

EaseMiss: HW/SW Co-Optimization for Efficient Large Matrix-M...

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15th IEEE Dallas Circuit and System Conference (DCAS)

作者： Nezhadi, Ali Angizi, Shaahin Roohi, Arman Univ Nebraska Sch Comp Lincoln NE 68588 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (数字)9781665498852

ISBN: (纸本)9781665498852

Due to the essential role of matrix multiplication in many scientific applications, especially in data and compute-intensive applications, we explore the efficiency of highly used matrix production algorithms. This paper proposes an HW/SW co-optimization technique, entitled EaseMiss, to reduce the cache miss ratio for large general matrix-matrix multiplications. First, we revise the algorithms by applying three software optimization techniques to improve performance. Choosing the proper algorithms to achieve the best performance is examined and formulated. By leveraging the proposed optimizations, the number of cache misses decreases by a factor of 3 in a conventional data cache. To further improve, we then propose SPLITCACHE to virtually split data cache regarding matrices' dimensions for better data reuse. This method can be easily embedded into conventional general-purpose processors or GPUs at the cost of negligible logical circuit overhead. After using the correct and valid splitting, the obtained results show that the cache misses reduce by a factor of 2 compared to the conventional data cache on average in the machine learning workloads.

关键词： Program processors Costs Circuits and systems Conferences software algorithms Production Machine learning

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WOGAN at the SBST 2022 CPS Tool Competition 15

WOGAN at the SBST 2022 CPS Tool Competition

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15th Search-Based software Testing Workshop (SBST)

作者： Peltomaki, Jarkko Spencer, Frankie Porres, Ivan Abo Akad Univ Turku Finland

WOGAN is an online test generation algorithm based on Wasser-stein generative adversarial networks. In this note, we present how WOGAN works and summarize its performance in the SBST 2022 CPS tool competition concerni... 详细信息

ISBN: (纸本)9781450393188

关键词： software testing Conferences software algorithms Generative adversarial networks Autonomous automobiles Test pattern generators Artificial intelligence

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Deep Direct Visual Servoing of Tendon-Driven Continuum Robots 18

Deep Direct Visual Servoing of Tendon-Driven Continuum Robot...

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IEEE 18th International Conference on Automation Science and Engineering (IEEE CASE)

作者： Abdulhafiz, Ibrahim Nazari, Ali A. Abbasi-Hashemi, Taha Jalali, Amir Zareinia, Kourosh Saeedi, Sajad Janabi-Sharifi, Farrokh Toronto Metropolitan Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Vision-based control provides a significant potential for the end-point positioning of continuum robots under physical sensing limitations. Traditional visual servoing requires feature extraction and tracking followed by full or partial pose estimation, limiting the controller's efficiency. We hypothesize that employing deep learning models and implementing direct visual servoing can effectively resolve the issue by eliminating such intermediate steps, enabling control of a continuum robot without requiring an exact system model. This paper presents the control of a single-section tendon-driven continuum robot using a modified VGG-16 deep learning network and an eye-in-hand direct visual servoing approach. The proposed algorithm is first developed in Blender software using only one input image of the target and then implemented on a real robot. The convergence and accuracy of the results in normal, shadowed, and occluded scenes demonstrate the effectiveness and robustness of the proposed controller.

关键词： Deep learning Limiting software algorithms Pose estimation Robot sensing systems Feature extraction Visual servoing

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Infra2Go: A Mobile Development Platform for Connected, Cooperative and Autonomous Driving 25

Infra2Go: A Mobile Development Platform for Connected, Coope...

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Fleck, Tobias Jauernig, Lennart Polley, Rupert Schorner, Philip Zofka, Marc Rene Zoellner, J. Marius FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany Karlsruhe Inst Technol KIT Karlsruhe Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Connected, cooperative autonomous driving and mobility promises increased comfort and safety for public transportation and logistics in urban and suburban regions. Stationary roadside infrastructure equipped with intelligent perception sensors and communication units has the potential to increase the field of view and mitigate occlusions in perception, but is limited to fixed places in the development process. We present a mobile development platform for connected roadside infrastructure that can be used in an extensible and flexible fashion for various purposes: as a traffic monitoring device, as a reference sensor platform to benchmark in-vehicle perception, as a test platform for collaborative perception algorithms and to operate as connected edge computation infrastructure to support vehicles in decision making in real world settings. In this work, we present an overview over the Infra2Go concept and give details about the hardware and software architecture. We evaluate our platform by deploying it in an outdoor real world scenario for cooperative perception between the platform and a vehicle equipped with an onboard V2X communication unit. The platform is used to track vulnerable road users (VRU) and transmits the information via V2X communication for cooperative perception (Cooperative Perception Messages) to an autonomous vehicle in order to extend it's sensor field of view.

关键词： Roads software algorithms Thermal sensors Sensor fusion Radar tracking Sensors Trajectory

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Decision tree-based Design Defects Detection

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IEEE ACCESS 2021年 9卷 71606-71614页

作者： Maddeh, Mohamed Ayouni, Sarra Alyahya, Sultan Hajjej, Fahima King Saud Univ Dept Informat Syst Riyadh 11451 Saudi Arabia Princess Nourah bint Abdulrahman Univ Dept Informat Syst Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia

Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.

关键词： Measurement Unified modeling language Object oriented modeling Decision trees software Predictive models software algorithms Anti-patterns bad smells decision tree model refactoring object oriented metrics

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