2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)最新文献_第10页

Images’ Partially Blurred Part Location and Restoration based on the Calculation Model and GAN Algorithm 基于计算模型和GAN算法的图像部分模糊部位定位与恢复

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040737

Tanghong Wu, Haiyi Mao, K. Xie, Y. Xie

{"title":"Images’ Partially Blurred Part Location and Restoration based on the Calculation Model and GAN Algorithm","authors":"Tanghong Wu, Haiyi Mao, K. Xie, Y. Xie","doi":"10.1109/ICSESS47205.2019.9040737","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040737","url":null,"abstract":"Digital images would be blurred due to defocus and motion of objects. A lot of researches have been done on the restoration of motion blurred images. Unlike the global motion blurred image, the local blurred image needs different methods to recover as there is both clear and blurred part in an image. In this paper, we proposed a method combining calculation model and Generative Adversarial Network (GAN), which can automatically identify the local blurred region and deblurring. Also, the blurred part can be filled back correctly. Firstly, the gradient image of the complete image is calculated based on the Sobel operator. The boundary information of the fuzzy region and the Gauss function variance of the blurred and clear region are obtained. And the power spectral gradient of the whole image and each local pixel block are further compared with the variance of the Gauss function. After the analysis is accomplished, a more accurate fixed position is achieved. After finishing extracting with clipping pixels, the extracted fuzzy region is input into the pre-trained Generative Adversarial Network to restore the local image. Finally, the alpha channel algorithm is used to calculate the RGB component without the alpha channel of the two images. The simulation results show the feasibility of this method.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140233","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 Semi-Partition Algorithm for Mixed-Criticality Tasks in Multiprocessor Platform 多处理器平台上混合临界任务的半分区算法

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040792

Lining Zeng, Yuan Lei, Yanxing Li

引用次数: 4

Remote Sensing Image Aircraft Detection Based on Feature Fusion across Deep Learning Framework 基于深度学习框架特征融合的遥感图像飞机检测

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040808

Wanjun Wei, Jiuwen Zhang, Chengyu Xu

{"title":"Remote Sensing Image Aircraft Detection Based on Feature Fusion across Deep Learning Framework","authors":"Wanjun Wei, Jiuwen Zhang, Chengyu Xu","doi":"10.1109/ICSESS47205.2019.9040808","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040808","url":null,"abstract":"The detection of remote sensing image aircraft targets based on deep learning has practical and important significance in the fields of military reconnaissance and disaster rescue. As a typical representative of the two mainstream detection algorithms, YOLOv3 and Faster_R_CNN have good detection effects on remote sensing image aircraft targets. However, for low quality remote sensing images, the two detection algorithms also have the phenomenons of omission and false detection. To deal with this, this paper proposes a target detection algorithm (YF_R_CNN) for „Separate training, joint detection‟, which realizes the cross-platform detection feature fusion of YOLOv3 based on darknet framework and Faster_R_CNN based on tensorflow framework, effectively alleviating the problems of existing algorithms. The experimental results show that the detection accuracy of YF_R_CNN algorithm reaches 94.8%, which is 3.7% and 3.1% higher than YOLOv3 and Faster_R_CNN respectively. The detection accuracy is obviously improved, and the algorithm has better flexibility and robustness.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914425","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

The Collaborative Identification of Design Flaws in Software Systems 软件系统中设计缺陷的协同识别

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040775

P. Thongkum, S. Mekruksavanich

引用次数: 0

ICSESS 2019 Author Index ICSESS 2019作者索引

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/icsess47205.2019.9040829

引用次数: 0

Classifier Selection Method Based on Multiple Diversity Measures 基于多多样性测度的分类器选择方法

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040785

Kefei Cheng, Zhiwen Song, Yanan Yue, Fengchi Shan, X. Guo

引用次数: 3

Task-oriented Dialogue System Based on Reinforcement Learning 基于强化学习的任务导向对话系统

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040789

Meina Song, Zhongfu Chen, Peiqing Niu, E. Haihong

{"title":"Task-oriented Dialogue System Based on Reinforcement Learning","authors":"Meina Song, Zhongfu Chen, Peiqing Niu, E. Haihong","doi":"10.1109/ICSESS47205.2019.9040789","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040789","url":null,"abstract":"In this paper, we propose a task-oriented dialogue system based on reinforcement learning. The overall system is composed of three parts: natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG). And our model can interact with the database in real time and acquire effective information from it. For the DM part, reinforcement learning is applied. Specially, we adopt an improved double deep Q-learning (DQN) strategy. In that case, the DM agent can resist the environmental noise considerably. Besides, we put forward a joint model for NLU module, and the experiments on ATIS and Snips datasets have proved the effectiveness of the joint model. For the overall system, the experiments are conducted on a public movie-ticket booking dataset. The experimental results indicate that the proposed model has outperformed the traditional rule-based multi-turn dialogue system both on simulated and real users. Besides, the double-DQN agent has better performance for both objective and subject evaluation, which demonstrates the effectiveness and superiority of our proposed model.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214964","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

Semantic Segmentation of Intracranial Hemorrhages in Head CT Scans 头颅CT扫描颅内出血的语义分割

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040733

Yuhang Qiu, Chia Shuo Chang, Jiun-Lin Yan, L. Ko, Tian Sheuan Chang

引用次数: 23

FDNN: Feature-based Deep Neural Network Model for Anomaly Detection of KPIs FDNN:基于特征的kpi异常检测深度神经网络模型

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040841

Zhibo Lan, Liutong Xu, Wei Fang

引用次数: 1

Emulation of Router Functions Based on A Cloud Platform 基于云平台的路由器功能仿真

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040817

Jianyu Chen, H. Song, Xiaofeng Wang, Sijie Yang

引用次数: 1