Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00107
Chen Li, D. Chrysostomou, Hongji Yang
This paper introduces a natural language-enabled virtual assistant (VA), called Max, developed to enhance human-robot interaction (HRI) with industrial robots. Regardless of the numerous natural language interfaces already available for commercial use and social robots, most VAs remain tightly bound to a specific robotic system. Besides, they lack a natural and efficient human-robot communication protocol to advance the user experience and the required robustness for use on the industrial floor. Therefore, the proposed framework is designed based on three key elements. A Client-Server style architecture that provides a centralised solution for managing and controlling various types of robots deployed on the shop floor. A communication protocol inspired by human-human conversation strategies, i.e., lexical-semantic strategy and general diversion strategy, is used to guide Max's response generation. These conversation strategies are embedded in Max's architecture to improve the engagement of the operators during the execution of industrial tasks. Finally, the state-of-the-art pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), is fine-tuned to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted for validating Max's performance in a real industrial environment.
{"title":"A Natural Language-enabled Virtual Assistant for Human-Robot Interaction in Industrial Environments","authors":"Chen Li, D. Chrysostomou, Hongji Yang","doi":"10.1109/QRS-C57518.2022.00107","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00107","url":null,"abstract":"This paper introduces a natural language-enabled virtual assistant (VA), called Max, developed to enhance human-robot interaction (HRI) with industrial robots. Regardless of the numerous natural language interfaces already available for commercial use and social robots, most VAs remain tightly bound to a specific robotic system. Besides, they lack a natural and efficient human-robot communication protocol to advance the user experience and the required robustness for use on the industrial floor. Therefore, the proposed framework is designed based on three key elements. A Client-Server style architecture that provides a centralised solution for managing and controlling various types of robots deployed on the shop floor. A communication protocol inspired by human-human conversation strategies, i.e., lexical-semantic strategy and general diversion strategy, is used to guide Max's response generation. These conversation strategies are embedded in Max's architecture to improve the engagement of the operators during the execution of industrial tasks. Finally, the state-of-the-art pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), is fine-tuned to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted for validating Max's performance in a real industrial environment.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431728","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00025
Bo Su, Zeyuan Zhang, Yuansheng Zhang, Qingyue Yang, Jiong Jiang
The world is currently facing a very serious energy crisis, how to save energy and improve energy utilization is an important issue to all countries. Lighting power consumption occupies a large proportion of people's electricity consumption, and most of the existing lighting control strategies are based on the activities of people or time scenes, ignoring the influence of natural light. In this paper, in order to make full use of natural light, we propose a real-time control algorithm of intelligent energy-saving lights. When natural light exists, the strategy calculates the level of light brightness at that moment through an algorithm and adjusts the brightness level of luminaire. This method saves energy by reducing the luminaire brightness level while meeting people's needs for work surface illumination. The simulation results indicated that our real-time lighting control algorithm has better energy saving effect compared with the traditional lighting control strategy.
{"title":"Real-Time Control Algorithm of Intelligent Energy-Saving Lights based on IoT","authors":"Bo Su, Zeyuan Zhang, Yuansheng Zhang, Qingyue Yang, Jiong Jiang","doi":"10.1109/QRS-C57518.2022.00025","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00025","url":null,"abstract":"The world is currently facing a very serious energy crisis, how to save energy and improve energy utilization is an important issue to all countries. Lighting power consumption occupies a large proportion of people's electricity consumption, and most of the existing lighting control strategies are based on the activities of people or time scenes, ignoring the influence of natural light. In this paper, in order to make full use of natural light, we propose a real-time control algorithm of intelligent energy-saving lights. When natural light exists, the strategy calculates the level of light brightness at that moment through an algorithm and adjusts the brightness level of luminaire. This method saves energy by reducing the luminaire brightness level while meeting people's needs for work surface illumination. The simulation results indicated that our real-time lighting control algorithm has better energy saving effect compared with the traditional lighting control strategy.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939279","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}
In recent years, Deep Learning systems have been applied to face recognition, autonomous vehicles and other safety-critical fields. Testing Deep Learning systems effectively and adequately is increasingly significant. In this paper, we proposed and implemented TSDTest, a coverage guided two-stage testing framework for deep learning systems. To test more logic for Deep Neuron Network (DNN), TSDTest generates highly diverse test cases with as high neuron coverage as possible during its two stages. Compared with DLFuzz, TSDTest achieved an average 1.75% improvement in neuron coverage and 80.3% more adversarial test inputs on MNIST and Fashion-MNIST. And the step dynamical adjustment also effectively reduces $l_{2}$ distance and avoids the manual identification of test oracle. The implementation of TSDTest shows its effectiveness and superiority in generating diverse test cases and improving the robustness of DNN.
{"title":"TSDTest: A Efficient Coverage Guided Two-Stage Testing for Deep Learning Systems","authors":"Hao Li, Shihai Wang, Tengfei Shi, Xinyue Fang, Jian Chen","doi":"10.1109/QRS-C57518.2022.00033","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00033","url":null,"abstract":"In recent years, Deep Learning systems have been applied to face recognition, autonomous vehicles and other safety-critical fields. Testing Deep Learning systems effectively and adequately is increasingly significant. In this paper, we proposed and implemented TSDTest, a coverage guided two-stage testing framework for deep learning systems. To test more logic for Deep Neuron Network (DNN), TSDTest generates highly diverse test cases with as high neuron coverage as possible during its two stages. Compared with DLFuzz, TSDTest achieved an average 1.75% improvement in neuron coverage and 80.3% more adversarial test inputs on MNIST and Fashion-MNIST. And the step dynamical adjustment also effectively reduces $l_{2}$ distance and avoids the manual identification of test oracle. The implementation of TSDTest shows its effectiveness and superiority in generating diverse test cases and improving the robustness of DNN.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736333","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00018
Shinya Haga, Taisei Takahashi, Kazumasa Omote
For solar power producers, fluctuation in power generation due to changes in solar radiation are one of the major risks because they can lead to unstable income. To deal with this risk, solar power producers have been trading weather derivatives, financial instruments that generate income calculated based on solar radiation. Prior research has proposed one-to-one bilateral swap-based weather derivatives utilizing DeFi. However, in bilateral transactions, the parties need to agree on various terms and conditions of the swap in advance, which may lead to lost time due to repeated negotiations and adjustments, as well as the possibility of failed negotiations. In this paper, we extend bilateral transactions to N-to-N multi-pair swaps, which do not require negotiation before the swap transaction and are easier to participate in. We implement a prototype on the Ethereum test network and show that our proposed method can mitigate income loss risk due to weather fluctuations.
{"title":"Multi Pair Swap-Based Weather Derivative DeFi","authors":"Shinya Haga, Taisei Takahashi, Kazumasa Omote","doi":"10.1109/QRS-C57518.2022.00018","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00018","url":null,"abstract":"For solar power producers, fluctuation in power generation due to changes in solar radiation are one of the major risks because they can lead to unstable income. To deal with this risk, solar power producers have been trading weather derivatives, financial instruments that generate income calculated based on solar radiation. Prior research has proposed one-to-one bilateral swap-based weather derivatives utilizing DeFi. However, in bilateral transactions, the parties need to agree on various terms and conditions of the swap in advance, which may lead to lost time due to repeated negotiations and adjustments, as well as the possibility of failed negotiations. In this paper, we extend bilateral transactions to N-to-N multi-pair swaps, which do not require negotiation before the swap transaction and are easier to participate in. We implement a prototype on the Ethereum test network and show that our proposed method can mitigate income loss risk due to weather fluctuations.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131009279","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00075
Guangwei Qing, Qianfei Zhou, Huifang Wang
In order to reduce the handling time of elevator failure and speed up the rescue of trapped personnel, an intelligent guidance method for elevator emergency treatment based on automatic recommendation of rescue units and prediction of fault causes is studied on the basis of elevator emergency treatment platform. The automatic recommendation module builds a multi-dimensional rescue unit capability evaluation index system, which establishes result recommendation methods such as recall, single-index recommendation, and comprehensive recommendation to achieve the optimal rescue unit recommendation for faulty elevators. The fault cause prediction module uses a variety of pre-trained word embedding models to vectorize fault text data on historical fault data sets, uses elevator fault text clustering algorithm based on attention mechanism and BI-LSTM model to obtain elevator fault labels, and uses the Boosting ensemble learning algorithm to construct an elevator fault prediction classification model for the marked elevator historical fault data set. The experimental results show that when the elevator fails, the automatic recommendation module can recommend the optimal rescue unit, and the fault prediction module can predict the cause of the elevator failure in real time, which quickly and accurately locates the fault area. For rescuers, it is convenient to deal with elevator failure in a targeted manner and greatly reduces the repair time. Therefore, this research is of great significance for speeding up rescue, improving emergency response capabilities, and ensuring the safe operation of elevators.
{"title":"Intelligent Guidance Method for Elevator Emergency Treatment based on Automatic Recommendation and Fault Prediction","authors":"Guangwei Qing, Qianfei Zhou, Huifang Wang","doi":"10.1109/QRS-C57518.2022.00075","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00075","url":null,"abstract":"In order to reduce the handling time of elevator failure and speed up the rescue of trapped personnel, an intelligent guidance method for elevator emergency treatment based on automatic recommendation of rescue units and prediction of fault causes is studied on the basis of elevator emergency treatment platform. The automatic recommendation module builds a multi-dimensional rescue unit capability evaluation index system, which establishes result recommendation methods such as recall, single-index recommendation, and comprehensive recommendation to achieve the optimal rescue unit recommendation for faulty elevators. The fault cause prediction module uses a variety of pre-trained word embedding models to vectorize fault text data on historical fault data sets, uses elevator fault text clustering algorithm based on attention mechanism and BI-LSTM model to obtain elevator fault labels, and uses the Boosting ensemble learning algorithm to construct an elevator fault prediction classification model for the marked elevator historical fault data set. The experimental results show that when the elevator fails, the automatic recommendation module can recommend the optimal rescue unit, and the fault prediction module can predict the cause of the elevator failure in real time, which quickly and accurately locates the fault area. For rescuers, it is convenient to deal with elevator failure in a targeted manner and greatly reduces the repair time. Therefore, this research is of great significance for speeding up rescue, improving emergency response capabilities, and ensuring the safe operation of elevators.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132268815","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00016
Irdin Pekaric, David Arnold, M. Felderer
Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.
{"title":"Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles using the Gazebo Simulator","authors":"Irdin Pekaric, David Arnold, M. Felderer","doi":"10.1109/QRS-C57518.2022.00016","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00016","url":null,"abstract":"Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133375468","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00072
Ning Wang
Public transportation is an important system of urban passenger transport. The purpose of this article is to explore the impact of network resilience when each station of urban rail transit network was attacked by large passenger flow. Based on the capacity load model, we propose a load redistribution mechanism to simulate the passenger flow propagation after being attacked by large passenger flow. Then, taking Xi'an's rail network as an example, we study the resilience variety of the network after a node is attacked by large passenger flow. Through some attack experiments, the feasibility of the model for studying the resilience of the rail transit system is finally verified.
{"title":"Resilience Analysis of Urban Rail Transit Network Under Large Passenger Flow","authors":"Ning Wang","doi":"10.1109/QRS-C57518.2022.00072","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00072","url":null,"abstract":"Public transportation is an important system of urban passenger transport. The purpose of this article is to explore the impact of network resilience when each station of urban rail transit network was attacked by large passenger flow. Based on the capacity load model, we propose a load redistribution mechanism to simulate the passenger flow propagation after being attacked by large passenger flow. Then, taking Xi'an's rail network as an example, we study the resilience variety of the network after a node is attacked by large passenger flow. Through some attack experiments, the feasibility of the model for studying the resilience of the rail transit system is finally verified.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116204424","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00053
Fangyuan Tian, Wenhong Liu, Shuang Zhao, Jiawei Liu
Deep learning models are affected by the training data when classifying, leading to discrimination in prediction output or disparity in prediction quality. We need to test the model adequately using a large amount of data. However, data for certain combinations of attributes occur less frequently in reality and are more difficult to obtain. Data augmentation is one of the methods to alleviate this problem. In this paper, we conduct a preliminary study on whether changes in these features(hair, glasses, bangs, etc.) could affect classification accuracy. This study provides some conclusions, (1) there is a fairness problem in the depth model (2) the fairness of the model can be well tested by auamentation against Image attributes.
{"title":"Face Recognition Fairness Assessment based on Data Augmentation: An Empirical Study","authors":"Fangyuan Tian, Wenhong Liu, Shuang Zhao, Jiawei Liu","doi":"10.1109/QRS-C57518.2022.00053","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00053","url":null,"abstract":"Deep learning models are affected by the training data when classifying, leading to discrimination in prediction output or disparity in prediction quality. We need to test the model adequately using a large amount of data. However, data for certain combinations of attributes occur less frequently in reality and are more difficult to obtain. Data augmentation is one of the methods to alleviate this problem. In this paper, we conduct a preliminary study on whether changes in these features(hair, glasses, bangs, etc.) could affect classification accuracy. This study provides some conclusions, (1) there is a fairness problem in the depth model (2) the fairness of the model can be well tested by auamentation against Image attributes.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116310280","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00054
Wenhong Liu, Zhiyuan Peng, Shuang Zhao, Jiawei Liu
With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining $W$ ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a $W$ ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.
{"title":"Similarity Analysis in Data Element Matching based on Word2vec","authors":"Wenhong Liu, Zhiyuan Peng, Shuang Zhao, Jiawei Liu","doi":"10.1109/QRS-C57518.2022.00054","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00054","url":null,"abstract":"With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining $W$ ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a $W$ ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129392856","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}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00106
Mengao Li, Hanting Zhao, Wenxiu Zhang, Xiqiao Pang, Tao Feng
Informatization of the ship industry is developing rapidly. In order to improve the software technology status management of the ship informatization system, a set of software technology management tools for the ship informatization system is developed, which is used for software version management control and problem feedback program establishment of the ship informatization system. At the same time, breakpoint continuation technology is used to realize the high stability software remote optimization and upgrading mechanism. Taking the ship management system on a large and medium-sized fishing boat of a fishery company as an example, we show that the software technology state management level and optimization iteration efficiency of the system have been greatly improved while the cost has been greatly reduced, which effectively promotes the efficient construction and rapid development of the ship information system.
{"title":"Software Technology Status Management under the Trend of Ship Informatization","authors":"Mengao Li, Hanting Zhao, Wenxiu Zhang, Xiqiao Pang, Tao Feng","doi":"10.1109/QRS-C57518.2022.00106","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00106","url":null,"abstract":"Informatization of the ship industry is developing rapidly. In order to improve the software technology status management of the ship informatization system, a set of software technology management tools for the ship informatization system is developed, which is used for software version management control and problem feedback program establishment of the ship informatization system. At the same time, breakpoint continuation technology is used to realize the high stability software remote optimization and upgrading mechanism. Taking the ship management system on a large and medium-sized fishing boat of a fishery company as an example, we show that the software technology state management level and optimization iteration efficiency of the system have been greatly improved while the cost has been greatly reduced, which effectively promotes the efficient construction and rapid development of the ship information system.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134521303","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：