2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)最新文献_第2页

Greedy Tuning Algorithm for Resource Scheduling of PISA Chips PISA芯片资源调度的贪心调优算法

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145929

Yikai Guo, Jie Liu, Zhiyu Duan, Zhizhao Luo, Wei Li, Bowei Ning

{"title":"Greedy Tuning Algorithm for Resource Scheduling of PISA Chips","authors":"Yikai Guo, Jie Liu, Zhiyu Duan, Zhizhao Luo, Wei Li, Bowei Ning","doi":"10.1109/IDITR57726.2023.10145929","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145929","url":null,"abstract":"PISA (Protocol Independent Switch Architecture) is one of the mainstream programmable chip architectures, allowing users to define the functions and behaviors of the data plane with PISA language. The PISA chip optimizes robot design by providing flexible data processing capabilities, reducing the difficulty of robot maintenance, and enhancing the interoperability of the robot with other devices or systems. However, the application of the PISA chip faces two challenges. (1) Due to the complex dependencies and constraints in the chip, the current chip resource allocation algorithms waste a lot of pipeline stages when executing basic blocks of programs. (2) The time complexity of existing resource scheduling algorithms is high, which affects the efficiency of the chip. To tackle these challenges, we introduce a low-complexity greedy tuning algorithm to schedule the resource of the chip. Firstly, we unify the data dependencies and control dependencies as path constraints, which are derived from the program flowchart and variable read-write information. Then the initial feasible schedule scheme is constructed under the given constraints. Finally, we optimize the initial feasible solution by designing a new data structure, namely segment flowchart, which efficiently solves the resource-sharing problem among basic blocks within the same pipeline stage. In a practical example, basic blocks are successfully placed into the pipeline under given constraints, saving 14.9% of pipeline stages compared with the sequential approach. Our code and dataset are publicly available.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123900348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMaglev: Graph-friendly Consistent Hashing for Distributed Social Graph Partition GMaglev:分布式社交图分区的图友好一致哈希

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145968

Miaomiao Cheng, Jiahui Zhang, Shuibing Long

引用次数: 0

Traffic Speed Forecasting: Comparison of Modeling Approaches 交通速度预测:建模方法的比较

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145819

Yu Cao, Zhou Huang, Xingchen Zhang, Gang Liu, R. Y. Hou

{"title":"Traffic Speed Forecasting: Comparison of Modeling Approaches","authors":"Yu Cao, Zhou Huang, Xingchen Zhang, Gang Liu, R. Y. Hou","doi":"10.1109/IDITR57726.2023.10145819","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145819","url":null,"abstract":"Over the past two decades, building an intelligent transportation system has become a popular and challenging research topic. As a key role in such a system, accurate traffic speed forecasting is critical. Although many powerful prediction methods have been proposed, they have not considered the application of models in real situations, that is, on various types of roads with different characteristics. So, we apply some representative and state-of-the-art methods on different types of roads to help people select the appropriate prediction method to construct an intelligent transportation system. First, we use the traffic data of 214 roads in Guangzhou in 61 days as the data set, and select four typical roads according to their characteristics of the roads. Then we use feature engineering to enhance the quality of the data set. After that, we apply Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing, short-term and long-term memory (LSTM) neural networks, and Informer on selected roads to make comparisons. The performance of models varies significantly in different types of roads: The Mean Absolute Error (MAE) for low mean and low variance roads is around 2, but the MAE for high mean and high variance roads is about 5. Notably, the Holt-Winters model shows the best performance in short-period prediction, and the Informer model offers the best performance in long-period prediction in the benchmarking.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130509833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Micro-Bristle Robot Design Via Different Surrogate Model Optimization Methods 基于不同代理模型优化方法的微刚毛机器人设计

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145817

Yifan Shi, Xiao Jing, Lushi Liu

引用次数: 0

Design of Fault Injector for Underwater Vehicle Sensors 水下航行器传感器故障注入器设计

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145975

Le Kang, Zhihua Chen, Tianyuan Yuan, Yunjiang Cheng

引用次数: 0

A Comparative Study of Cross-Sentence Features for Named Entity Recognition 命名实体识别的跨句特征比较研究

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145820

Sheng-Fu Wang, Jing Huang, Baohua Zhang, Jia Li

{"title":"A Comparative Study of Cross-Sentence Features for Named Entity Recognition","authors":"Sheng-Fu Wang, Jing Huang, Baohua Zhang, Jia Li","doi":"10.1109/IDITR57726.2023.10145820","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145820","url":null,"abstract":"Recently, a growing number of Named Entity Recognition (NER) methods utilize cross-sentence features (also known as contexts) to improve the performance of NER models, instead of using single-sentence information alone. As far as we know, most NER models choose to exploit pre- and post-sentences to capture cross-sentence features. Generally, current NER studies focus only on the model architecture to capture better token representations. However, there is no in-depth exploration on how to better model cross-sentence features. In this paper, based on the span classification model, we investigate the effect of cross-sentence features under different settings. Specifically, we evaluate the impact of context stitching, context window size, context window padding, and classifier token of pre-trained language model (PLM) on model performance. Comparative experimental results show that appropriate incorporation of document-level contexts can considerably improve the NER metrics. Furthermore, we find that several factors can be used to improve the performance of NER models: (1) use domain-specific PLMs, but not classifier tokens; (2) use only preceding contexts for generic text, and random contexts for specialized text; (3) truncate overly long contexts when the context window is small, and preserve sentence integrity when the window is large; (4) set the context window size to about 200 for the basic size PLM.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115850466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RDIDI: Recognition of Defect Image Detection in Industry RDIDI:工业缺陷图像检测识别

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145941

Xusen Lang, Chenyao Bai, Yunlong Zhu

{"title":"RDIDI: Recognition of Defect Image Detection in Industry","authors":"Xusen Lang, Chenyao Bai, Yunlong Zhu","doi":"10.1109/IDITR57726.2023.10145941","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145941","url":null,"abstract":"Electronic consumer products are closely related to life. With the increase of demand, various consumer electronic products have emerged as the times require. For some high-precision equipment, such as in all aspects of the chip manufacturing process, the requirements for PCB are relatively high. In the PCB manufacturing process, it is often due to various problems caused by improper operation of certain links in the process. Many defects may appear on the PCB, such as bubbles appearing when the film is not firmly attached to the ground during the film attachment process, and bubbles appear during the exposure process. Negative film scratches, excessive pressure during etching, unevenness during plating, etc. Printed board surface defects vary in size. This inconsistency of multi-scale features will cause the pooling operation of the network model to lose some fine-grained spatial features. In the field of object detection, in the past, the detection effect in the field of PCB defect detection was poor, and the natural defects were few and small, and faced some bottlenecks in engineering. Aiming at this problem, a PCB defect detection method based on RDIDet is proposed. The experimental results prove that the improved network has obvious performance advantages over the previous classic model, with an accuracy rate of 98.3%, and a better detection effect on PCB defects.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125055983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crane Hook Motion Monitoring Based on Trajectory Filtering and Tracking Video Monitoring 基于轨迹滤波和跟踪视频监控的起重机吊钩运动监控

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145970

Jin Li, Z. Lv, Jinhui You, Wei Guo

引用次数: 0

Design of a Constant Flow Control System for Cut Tobacco Feeder 烟丝给料机恒流量控制系统的设计

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145973

Yongqiang Gu, Shangrong Wang, Shaolong Han, Ruixu Han

{"title":"Design of a Constant Flow Control System for Cut Tobacco Feeder","authors":"Yongqiang Gu, Shangrong Wang, Shaolong Han, Ruixu Han","doi":"10.1109/IDITR57726.2023.10145973","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145973","url":null,"abstract":"The speed control of the climbing belt motor of the cut tobacco feeder does not match the demand of the cut tobacco feeding flow, and there are a series of problems such as large fluctuation of the mass flow, cut tobacco shortage, cut tobacco blockage, climbing belt motor start or stop frequently, large equipment loss and energy consumption. An improved speed control method of the climbing belt motor is proposed to solve the above problems. Firstly, the relationship between motor frequency and mass flow is modeled and analyzed; Then, a reasonable frequency modulation control logic is proposed; Finally, the model and control logic are verified and further improved based on production data. The experimental results show that: After improvement, the average starting or stopping times of the motor are reduced by 98.61%, the average high-frequency triggering times are reduced by 96.48%, and average value of flow range in the head stage is reduced by 75.78%. While improving the stability of the cut tobacco mass flow, the stability of the climbing belt motor has also been greatly improved, which not only eliminates the hidden danger of cut tobacco shortage and blockage, but also reduces the equipment loss and energy consumption.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115727658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Cell Diffraction Images Based on Deep Neural Network 基于深度神经网络的细胞衍射图像分类

2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) Pub Date : 2023-05-01 DOI: 10.1109/IDITR57726.2023.10145988

Xi-kun Zhang, Jie Hou

{"title":"Classification of Cell Diffraction Images Based on Deep Neural Network","authors":"Xi-kun Zhang, Jie Hou","doi":"10.1109/IDITR57726.2023.10145988","DOIUrl":"https://doi.org/10.1109/IDITR57726.2023.10145988","url":null,"abstract":"With the continuous advancement of the life sciences and the advancement of ultra-high resolution technology, people can observe biological information such as organelles and molecules, and can understand their internal structure and interaction from the acquired cell diffraction images. However, cells contain various types of organelles, which have high heterogeneity, and the cell structures of different types of cells have certain differences. Therefore, the study of cell diffraction image classification is of great significance in many fields such as cell morphology and cell biology. The research task of image classification is to extract useful feature information from the image, and then distinguish the images of different attributes, and finally divide the image targets of different categories. Deep learning techniques are used in a variety of industries, including picture categorization, as a result of the development of deep learning. Among these, there has been a notable improvement in the accuracy of picture classification using deep neural networks. The classification accuracy can be further increased in the real cell diffraction image classification procedure, though. This study proposes a deep neural network-based classification strategy for cell diffraction pictures. For the cell diffraction images, some interference pictures are created by cell debris or impurities. In this work, the produced diffraction pictures are preprocessed using a clustering approach and a support vector machine (SVM).After that, a Gray Level Co-occurrence Matrix(GLCM) is used to extract the texture features of the diffraction image. This research proposes an enhanced deep neural network-based picture classification algorithm. Max-margin Minimum Classification Error (M3CE) is introduced during the training of deep neural networks, and cross-entropy is used to build the loss function. Finally, the Ramos and Jurkat cell experiments' findings support the high precision of the categorization approach.","PeriodicalId":272880,"journal":{"name":"2023 2nd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0