Proceedings of the 4th International Conference on Computer Science and Application Engineering最新文献_第2页

Improvement and Design of Genetic Algorithm in Personalized Test Paper Composition System 遗传算法在个性化考卷系统中的改进与设计

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425008

Liping Ma, Xun Zhu, Q. Feng

引用次数: 1

Short-term Power Load Forecasting Based on Gate Recurrent Unit Network and Cloud Computing Platform 基于门循环单元网络和云计算平台的短期电力负荷预测

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425007

Xiaohua Li, Weijin Zhuang, Hong Zhang

{"title":"Short-term Power Load Forecasting Based on Gate Recurrent Unit Network and Cloud Computing Platform","authors":"Xiaohua Li, Weijin Zhuang, Hong Zhang","doi":"10.1145/3424978.3425007","DOIUrl":"https://doi.org/10.1145/3424978.3425007","url":null,"abstract":"Short-term power load forecasting plays a very important role in the entire smart grid system. The results of short-term power load forecasting have a great impact on the scheduling and production of power systems. Accurate and efficient short-term power load forecasting can help improve the safety and stability of power systems. Therefore, the design of the forecasting algorithm has always been a very core research direction in the field of power systems. Traditional forecasting methods cannot take into account both the time series and non-linear characteristics of the power load data when performing shortterm power load forecasting. To tackle this problem, we propose a short-term power load forecasting method based on Gate Recurrent Unit (GRU) to predict the power load. Moreover, given that the cloud computing platform can provide parallel computing capabilities and large-scale data storage capabilities, we build our model based on cloud computing methods. We conducted extensive experiments and compared our prediction results with traditional methods to demonstrate that our method is much more accurate and efficient.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124143151","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: 6

Optimal Design of Filter for Digital Power Amplifier in Vehicle-ground System 车地系统中数字功率放大器滤波器的优化设计

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425153

Shuai Han, Pengfei Dai, H. Ding, Xiguo Ren

引用次数: 0

Analysis and Recognition of Heart Sound Based on NCS2 Neural Computing Stick 基于NCS2神经计算棒的心音分析与识别

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425045

Ke Sun, Weilian Wang, Ruping Yao, Jiahua Pan, Hongbo Yang

{"title":"Analysis and Recognition of Heart Sound Based on NCS2 Neural Computing Stick","authors":"Ke Sun, Weilian Wang, Ruping Yao, Jiahua Pan, Hongbo Yang","doi":"10.1145/3424978.3425045","DOIUrl":"https://doi.org/10.1145/3424978.3425045","url":null,"abstract":"At present, the recognition and analysis of heart sound signal was usually run by using high-performance PC. It was hardly done by embedded devices due to limited resources. Provide a portable device for assisting in the initial diagnosis of congenital heart disease (CHD) for doctors with outdated equipment in remote mountainous areas. A novel embedded heart sound analysis and recognition system based on Raspberry pi 3b+ with a NCS2 neural computing stick was put forward in this paper. Firstly, the OpenVINO software platform launched by Intel was used to transfer the ssd_inception_v2 model into the Raspberry Pi after performing transfer learning optimization. Then, reasoning calculation was carried out in Raspberry pi with neural computing stick. Neural computing stick is a deep learning and reasoning tool based on USB mode and an independent artificial intelligence accelerator. NCS2 neural computing stick was used to realize the heart sound analysis and recognition of embedded devices. The sensitivity of the experimental results is 80.7%, the specificity is 95.5%, and the accuracy is 91.4%. The experimental results show that the system has the advantages of low power dissipation, low cost, small size, fast speed, and high recognition rate. It can be used for machine assisted diagnosis of congenital heart disease.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121144","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

Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with Machine Learning 基于机器学习的中国2型糖尿病特征选择与预测模型

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425085

Jiaqi Hou, Yongsheng Sang, Yuping Liu, Li Lu

{"title":"Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with Machine Learning","authors":"Jiaqi Hou, Yongsheng Sang, Yuping Liu, Li Lu","doi":"10.1145/3424978.3425085","DOIUrl":"https://doi.org/10.1145/3424978.3425085","url":null,"abstract":"Diabetes is a chronic disease characterized by hyperglycemia. Based on the rising incidence of the disease in recent years, diabetes is affecting more and more families. In 2017 alone, it caused 5 million deaths and cost $850 billion in global healthcare. In this paper, we proposed a method to predict the prevalence of diabetes based on a selected set of features from physical examination data. We used Fisher's score, RFE and decision tree to select features. Random forest, logistic regression, SVM and MLP were used to predict the prevalence of diabetes. EA and Fisher' s score helped us to reduce dimensions. We used random forest to classify diabetes accurately. Our results show that the highest accuracy (0.987) can be achieved by using random forest with 85 features. The prediction accuracy using Fisher's Score with 19 features also reached 0.986. We finally selected 5 features based on our method to form a new dataset for diabetes prediction. The 5 features are fasting plasma glucose, HbA1c, HDL, total cholesterol level and hypertension. The values of accuracy, precision, sensitivity, F1 score, MCC and AUC were 0.977, 0.968, 0.812, 0.883, 0.875, and 0.905, respectively. Results show that our method can be successfully used to select features for diabetes classifier and improve its performance, which will provide support for clinicians to quickly identify diabetes.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439958","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: 6

Evaluation of the Learning Performance for Virtual Simulation Experiment 虚拟仿真实验学习性能的评价

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425151

Fuan Wen, Zhimin Ji

{"title":"Evaluation of the Learning Performance for Virtual Simulation Experiment","authors":"Fuan Wen, Zhimin Ji","doi":"10.1145/3424978.3425151","DOIUrl":"https://doi.org/10.1145/3424978.3425151","url":null,"abstract":"Evaluation is an important way to test the learning performance. Due to its advantages of intuition, interactivity, imagination, repeatability and non-dangerousness, virtual simulation experiments have been widely used in experimental teaching. It can complete experiments that cannot be done by traditional experiments, such as high-risk, high-consumption, unreachable and irreversible experiments. With the promotion and application, the learning performance of virtual simulation experiments is more and more concerned by teachers. The result has been brought is that the rough and fuzzy experimental feedback reduces students' learning efficiency and interest. This paper focuses on the method and content of evaluation in the virtual simulation experiment, and aims to ensure the learning performance of students. Paper proposes a set of technical evaluation indicators suitable for the evaluation content. The object of evaluation is the virtual simulation experiment. This evaluation method called \"meta-evaluation\". There are three main indicators, namely \"objective evaluation\", \"subjective evaluation\" and \"assessment effectiveness\". The five secondary indicators namely \"have experimental process\" and \"have operational evaluation function\" etc. In order to verify the scientificity and applicability of the indicators, paper has selected 527 items as the test objects, and obtained a series of scientific and objective evaluation data, hoping to provide references for virtual simulation experiment research.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951223","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

Text Detection of Clinical Medical Documents Based on SWT Algorithm 基于SWT算法的临床医学文献文本检测

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425119

Jingyi Wang, Zhao Liu

引用次数: 0

Gait Phase Detection of Exoskeleton Robot Based on Optimized DAG-SVM 基于优化DAG-SVM的外骨骼机器人步态相位检测

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425081

Shuaishuai Hu, Jianbin Zheng, Liping Huang

引用次数: 0

Research on Monitoring Method of Fuel Consumption for Large and Medium-sized Vehicles Based on Sensor Network 基于传感器网络的大中型车辆油耗监测方法研究

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425159

Guohua Yue, Jingting Wang

{"title":"Research on Monitoring Method of Fuel Consumption for Large and Medium-sized Vehicles Based on Sensor Network","authors":"Guohua Yue, Jingting Wang","doi":"10.1145/3424978.3425159","DOIUrl":"https://doi.org/10.1145/3424978.3425159","url":null,"abstract":"Application of fuel tank status monitoring technology is studied during the operation of large and medium-sized vehicles. By means of sensor network technology, the vehicle on-board terminal periodic data was collected. Vehicle fuel oil of sensing historical data was analyzed and processed. Recognition algorithm of fuel state is studied in order that the status of each of the fuel sampling points are identified as 4 states: \"Using, oil filling, oil leaking, Stopping\", in order to automatically calculate and perceive the vehicle filling time, oil filling value, oil leaking time, oil leaking value and daily fuel oil consumption value. To overseeing and decision of illegal acts of drivers and conductors such as overstatement of the amount of fuel, stealing and selling fuel to provide the scientific basis for the decision to reduce the loss of company property. On the basis of the fuel status recognition, the calculated results and the actual vehicle oil filling data are analyzed and compared, indicating that the vehicle fuel status recognition algorithm can determine the state of each fuel sample point correctly. Calculated value compared to the actual oil filling values and fuel consumption values, errors between them are no more than 3%, meeting the requirements of the fuel control. Vehicle fuel Status recognition algorithm for each oil sample point fuel condition was the correct judgment, then monitoring fuel calculation and amount of oil used to control the enterprise cost of fuel. The result is satisfactory.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131884161","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 BLDCM Double Loop PWM Control Based on Positionless Sensor 基于无位置传感器的无刷直流电机双环PWM控制研究

Proceedings of the 4th International Conference on Computer Science and Application Engineering Pub Date : 2020-10-20 DOI: 10.1145/3424978.3425065

Nan Zhang, Hao Jiang, Chunbao Xu, Xuemei Liu, Zekun Quan, Yinfa Yan, Shuangxi Liu

引用次数: 0