2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)最新文献_第4页

Answering to 5W Using Digital Forensics Data 使用数字取证数据回答5W

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00043

Carmelo Ferrante, Babak Habibnia

引用次数: 0

Searching for Parking in a Busy Downtown District: An Agent-based Computational and Analytical Model 繁忙市区停车搜索:基于agent的计算与分析模型

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00069

Nilankur Dutta, Alexandre Nicolas

{"title":"Searching for Parking in a Busy Downtown District: An Agent-based Computational and Analytical Model","authors":"Nilankur Dutta, Alexandre Nicolas","doi":"10.1109/ISCSIC54682.2021.00069","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00069","url":null,"abstract":"Finding a place to park one's car is a serious issue in contemporary urban mobility. Despite the importance of the topic (30% of cars might be cruising for parking in many large cities) and the central role given to parking policies, surprisingly little is known about the basic laws governing the search time. We present a novel agent-based approach combining numerical simulations and theoretical considerations to model cars cruising for on-street parking in busy downtown districts. The approach is premised on the idea that, rather than parking at the first vacant spot that they encounter, drivers may be more or less prone to parking on a given spot, depending on their perceptions of its characteristics (notably its distance to their destination and its cost). This spot-specific parking probability is quantified by means of a scalar variable, the ‘attractiveness’. On this premise, we show that this problem can be solved using an exact formula for the stationary state and depends on the topology of the streets. This is demonstrated by comparing our theoretical results with a stochastic in-silico model and the method is illustrated with the case of the city centre of Lyon. Finally, the relationship between the search time and the spatial modulation of the attractiveness of parking spots is explored. We find that such a modulation, which could in practice be enforced by targeted parking policies at the level of individual streets, dramatically affects the parking search time, which paves the way for a more efficient control over occupancies and cruising times in on-street parking networks.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130400231","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: 2

Adjustment Model of CPIII Height Control Network which Taking Vertical Angle as Observation Value 以垂直角度为观测值的CPIII高度控制网平差模型

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00062

Jianzhang Li

引用次数: 0

Near-Real Time Quality Prediction in a Plastic Injection Molding Process Using Apache Spark 使用 Apache Spark 对注塑成型工艺进行近乎实时的质量预测

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00059

Enes Uguroglu

{"title":"Near-Real Time Quality Prediction in a Plastic Injection Molding Process Using Apache Spark","authors":"Enes Uguroglu","doi":"10.1109/ISCSIC54682.2021.00059","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00059","url":null,"abstract":"The automotive industry is undergoing wide scope transformation. Industry 4.0 has both expanded the possibilities of digital transformation in automotive, increased its importance to all mobility ecosystem and being driven by continued digitization of the entire value chain. Manufacturing data which is unceasingly flow during serial production is one of the great sources towards Industry 4.0 goal to fully automatizing complex human dependent processes. However, there are few challenges to consider such as collecting and filtering various data from shop floor in given production cycle time range and make them ready for real time analytics as well as constructing efficient data pipeline to reach useful outcomes which is reliable enough to meet customer expectations. In this study, we will extract meaningful relation between injection machine parameters from Farplas Automotive Company's shop floor and describe their effects on the product quality. We will train and test machine learning models with different hyperparameters and test model performance to identify defected products. Finally, we will show implementation of streaming data pipeline using Kafka and Spark to be able to analyze injection machine data and effectively predict plastic injection product's OK-NOK condition real time even before human operator reaches the product itself. Consequently, detecting defected products will be independent from human attention which makes production areas one step closer to dark factory.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134221614","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: 1

An Intelligent Fault Diagnosis Method Of Marine Seawater Cooling System Based On SOM Neural Network 基于SOM神经网络的船用海水冷却系统故障智能诊断方法

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00050

Lei Guo, Jun-dong Zhang, Y. Zou, Guochang Qi, Keyu Guo, Yanghui Tan

引用次数: 1

A Novel Multi-Model Machine Learning Approach to Real-Time Road Accident Prediction and Driving Behavior Analysis 基于多模型机器学习的道路交通事故实时预测与驾驶行为分析

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00023

Diya Dinesh

{"title":"A Novel Multi-Model Machine Learning Approach to Real-Time Road Accident Prediction and Driving Behavior Analysis","authors":"Diya Dinesh","doi":"10.1109/ISCSIC54682.2021.00023","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00023","url":null,"abstract":"As the leading cause of death within the U.S, road accidents take over 38,000 lives every year. Efforts are being taken nationwide to reduce the accidents and fatalities. Previous studies on the use of computer science for road safety were centered around analysis of historical data and prediction. This study proposes a novel solution, including real-time updates and features to road safety, with the use of Artificial Intelligence and Deep Learning integrated with various APIs and statistical analyses through the RoadSafety application. This application consists of three features: accident risk prediction, landmark analysis, and driving behavior analysis. The accident risk prediction component consists of a fully connected feed-forward deep neural network that takes in location, weather, time, and road feature input to predict an accident risk level. The landmark analysis identifies, through usage of the Pearson correlation coefficient and recursive feature elimination, which types of locations/landmarks are best correlated with accident severity. The driving behavior analysis uses an object detection Core ML model and the pinhole projection formula to identify distance from the driver to an obstacle ahead. This feature also compares the driver's speed to the speed limit. All three features are integrated into an iOS application to provide drivers within D.C. with live updates on accident prone-zones, landmark indicators of high accident severity, and risky driving behaviors.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129338156","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

A Lightweight Autonomous Vehicle System Based On Pure Visual Navigation 基于纯视觉导航的轻型自动驾驶汽车系统

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00063

Zuojun Fu, Yi Hou, Cangjian Liu, Yi Zhang, Shilin Zhou

{"title":"A Lightweight Autonomous Vehicle System Based On Pure Visual Navigation","authors":"Zuojun Fu, Yi Hou, Cangjian Liu, Yi Zhang, Shilin Zhou","doi":"10.1109/ISCSIC54682.2021.00063","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00063","url":null,"abstract":"In recent years, autonomous driving solutions that rely on the fusion of multiple sensors such as lidar, inertial navigation systems, and GPS positioning systems have attracted the attention of the industry and academia. Such solutions use multiple sets of sensing systems to work together to ensure the greatest probability of the accuracy and completeness of the results. These solutions are able to tackle problems that are difficult to solve with a single sensor in the short term. However, in the long run, the design of deep coupling between data and strategy is not conducive to the real improvement of the perception system. Therefore, this paper proposes a light-weight autonomous vehicle system based on pure vision named VOLWA. To cope with the huge challenge of using the camera as the only sensor, VOLWA contains three new algorithms:(i) view segmentation algorithm is proposed to improve the perception accuracy (ii) visual place recognition algorithm for removing dynamic targets based on channel selection is proposed to solve the interference problem of dynamic targets such as pedestrians and vehicles in the field of view (iii) threshold self-localization based on image features is proposed for high-precision positioning of autonomous vehicles. Experimental results show that visual place recognition algorithm for removing dynamic targets based on channel selection effectively improves the performance of place recognition. The system architecture and algorithm proposed in this paper have been transplanted to self-developed autonomous vehicles for verification, and the autonomous vehicles are running well. See the link for the demo video https://youtu.be/6WfUnor205M.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867275","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: 1

Very Low-Level Airspace Assessment and Classification for Unmanned Aircraft Operation 无人机运行的低空空域评估与分类

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00013

Qinggang Wu, Jianping Zhang, Yiqian Zhong, Weidong Liu, Xiang Zou, Fangquan Xie

{"title":"Very Low-Level Airspace Assessment and Classification for Unmanned Aircraft Operation","authors":"Qinggang Wu, Jianping Zhang, Yiqian Zhong, Weidong Liu, Xiang Zou, Fangquan Xie","doi":"10.1109/ISCSIC54682.2021.00013","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00013","url":null,"abstract":"With the increasing activity of unmanned aircraft (UA) in very low-level (VLL) airspace, severe challenges have emerged to personal safety, social order and national security. This paper proposes a method of airspace assessment and classification for UA operation. Both ground and air risks caused by UA are taken into considered to establish a quantifiable VLL airspace assessment indication system. Then, the comprehensive risk value of each grid of rasterization airspace is estimated by a set of comprehensive evaluation method. the concept of additional characteristic value is presented to reduce the threat to high risk level areas caused by surrounding UAs. Finally, the VLL airspace for UAs is organized into three classifications defined in this paper: permitted fly area, limited fly area and forbidden fly area. A case study on the simulated VLL urban airspace illustrates that the proposed method can assess the operational risk of UA and classify VLL airspace to set different access criteria for UA. Based on the three classifications of airspace defined in this paper, the efficiency and risks of UA can be balanced by taking adequate mitigation measures and permit UA to enter expanded airspace.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128922782","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

Research on Stall Recovery Parachute Flight Deployment Tests 失速回收降落伞飞行展开试验研究

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00016

Zhijie Qiu, Wei Dai, Gaozhi Guan

引用次数: 0

Weibo Sentiment Analysis Based on Advanced Capsule Network 基于高级胶囊网络的微博情感分析

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00046

Kehua Yang, Jing Liu

{"title":"Weibo Sentiment Analysis Based on Advanced Capsule Network","authors":"Kehua Yang, Jing Liu","doi":"10.1109/ISCSIC54682.2021.00046","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00046","url":null,"abstract":"Sentiment analysis is to discover and prove the individual's emotions for a partic-ular content, mainly to analyze the emotions and opinions in the text. Social media contains a large amount of sentiment data, and sentiment analysis of these large amounts of generated data can reflect the opinions of the public. Traditional text sentiment analysis methods mainly study extended texts, such as news reports and complete text files. However, sentiment analysis on Douban film reviews and Weibo is more difficult than general sentiment analysis, because his language is not standardized and mostly spoken. Accompanied by typos, acronyms, Internet language, and correct recognition of the emotion of each word is very important. Previous sentiment classification methods have poor classification results when processing short texts like Weibo, because they often fail to extract obvious features. In our model, the newly proposed BERT model and the capsule network model are combined. The word vector generated by the input layer in the BERT model and the word vector obtained through the attention mechanism are con-nected, and at the same time with the feature extraction based on the capsule net-work In combination, it is used for the latest reviews of movies in the Weibo top-ic, which is called an advanced capsule network. This model is used to divide reviews into positive reviews and negative reviews, and judge the accuracy of their classification based on a series of evaluation criteria. The experimental results show that the use of our advanced capsule network model is more accurate in Weibo texts, and can well reflect the reputation of movies.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131139585","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