Pub Date : 2017-05-15DOI: 10.1109/SIU.2017.7960547
G. Memis, M. Sert, A. Yazıcı
Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.
{"title":"Leveraging multimodal and feature selection approaches to improve sleep apnea classification performance","authors":"G. Memis, M. Sert, A. Yazıcı","doi":"10.1109/SIU.2017.7960547","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960547","url":null,"abstract":"Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125243005","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960299
E. Aydin, S. E. Yüksel
Deep learning has started to outperform its rivals over the last five years, due to its capability to automatically find the features in the data, and classify them. In this study, deep learning is used to detect a buried target collected by a ground penetrating radar (GPR). The GPR data is generated by the GprMax simulation program, and a deep learning model of two convolution and two pooling layers is proposed to classify this data. The proposed model is trained with two classes, with a hundred targeted targets and a hundred non-targets. At the end of the training, the resulting features were examined in each layer of the deep architecture. The initial results presented in this study emphasize the advantages of deep learning over traditional classification methods, since it allows for high classification rates without the need for feature extraction.
{"title":"Buried target detection with ground penetrating radar using deep learning method","authors":"E. Aydin, S. E. Yüksel","doi":"10.1109/SIU.2017.7960299","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960299","url":null,"abstract":"Deep learning has started to outperform its rivals over the last five years, due to its capability to automatically find the features in the data, and classify them. In this study, deep learning is used to detect a buried target collected by a ground penetrating radar (GPR). The GPR data is generated by the GprMax simulation program, and a deep learning model of two convolution and two pooling layers is proposed to classify this data. The proposed model is trained with two classes, with a hundred targeted targets and a hundred non-targets. At the end of the training, the resulting features were examined in each layer of the deep architecture. The initial results presented in this study emphasize the advantages of deep learning over traditional classification methods, since it allows for high classification rates without the need for feature extraction.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126027621","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960397
Zafer Cömert, A. F. Kocamaz
Fetal heart rate (FHR) has notable patterns for the assessment of fetal physiology and typical stress conditions. FHR signals are obtained using cardiotocography (CTG) devices also providing uterine activities simultaneously and fetal movements. In this study, a total of 88 records consisting of 44 normal and 44 hypoxic fetuses instances obtained from publicly available CTU-UHB database have been considered. The basic morphological features supporting clinical diagnosis, the powers of 4 different spectral bands and Lempel Ziv complexity have been used to define FHR signals. Also, it has been proposed to use segmentation-based fractal texture analysis (SFTA) to identify the signals more accurately. The obtained feature set was applied as the input to extreme learning machine (ELM) with 5-fold cross-validation method. According to experimental results, 79.65% of accuracy, 79.92% of specificity, and 80.95% of sensitivity were obtained. It was observed that the SFTA offers useful statistical features to distinguish normal and hypoxic fetuses.
{"title":"Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine","authors":"Zafer Cömert, A. F. Kocamaz","doi":"10.1109/SIU.2017.7960397","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960397","url":null,"abstract":"Fetal heart rate (FHR) has notable patterns for the assessment of fetal physiology and typical stress conditions. FHR signals are obtained using cardiotocography (CTG) devices also providing uterine activities simultaneously and fetal movements. In this study, a total of 88 records consisting of 44 normal and 44 hypoxic fetuses instances obtained from publicly available CTU-UHB database have been considered. The basic morphological features supporting clinical diagnosis, the powers of 4 different spectral bands and Lempel Ziv complexity have been used to define FHR signals. Also, it has been proposed to use segmentation-based fractal texture analysis (SFTA) to identify the signals more accurately. The obtained feature set was applied as the input to extreme learning machine (ELM) with 5-fold cross-validation method. According to experimental results, 79.65% of accuracy, 79.92% of specificity, and 80.95% of sensitivity were obtained. It was observed that the SFTA offers useful statistical features to distinguish normal and hypoxic fetuses.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394158","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960721
Iskender Ulgen Ogul, Caner Ozcan, Ozlem Hakdagli
The increase in the number of devices and users online with the transition of Internet of Things (IoT), increases the amount of large data exponentially. Classification of ascending data, deletion of irrelevant data, and meaning extraction have reached vital importance in today's standards. Analysis can be done in various variations such as Classification of text on text data, analysis of spam, personality analysis. In this study, fast text classification was performed with machine learning on Apache Spark using the Naive Bayes method. Spark architecture uses a distributed in-memory data collection instead of a distributed data structure presented in Hadoop architecture to provide fast storage and analysis of data. Analyzes were made on the interpretation data of the Reddit which is open source social news site by using the Naive Bayes method. The results are presented in tables and graphs
{"title":"Fast text classification with Naive Bayes method on Apache Spark","authors":"Iskender Ulgen Ogul, Caner Ozcan, Ozlem Hakdagli","doi":"10.1109/SIU.2017.7960721","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960721","url":null,"abstract":"The increase in the number of devices and users online with the transition of Internet of Things (IoT), increases the amount of large data exponentially. Classification of ascending data, deletion of irrelevant data, and meaning extraction have reached vital importance in today's standards. Analysis can be done in various variations such as Classification of text on text data, analysis of spam, personality analysis. In this study, fast text classification was performed with machine learning on Apache Spark using the Naive Bayes method. Spark architecture uses a distributed in-memory data collection instead of a distributed data structure presented in Hadoop architecture to provide fast storage and analysis of data. Analyzes were made on the interpretation data of the Reddit which is open source social news site by using the Naive Bayes method. The results are presented in tables and graphs","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123911169","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960656
Fahrettin Ay, I. Engin, G. Ince
Along with the widespread use of technology, there have been many studies on human-computer relation recently. Study of the effects of computer systems on human education can be shown as an example of this. In this paper, a reliable and real time tangible user interface system developed for the air drum game which is used both for game and computer based training programs is presented. A user can perform a play action in six different directions using electronic drum sticks. A developed algorithm detects the direction of moving stick by processing sensor data taken from these sticks. Information about the stick which is moved and its direction are sent to the sound system to play the sound file of the relevant drum instrument. In this way the user is presented with a realistic drum experience. The developed system has been tested by different users in real world and its performance has been reported. The results verify the reliability and usability of the electronic drum game.
{"title":"A tangible user interface for air drum game","authors":"Fahrettin Ay, I. Engin, G. Ince","doi":"10.1109/SIU.2017.7960656","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960656","url":null,"abstract":"Along with the widespread use of technology, there have been many studies on human-computer relation recently. Study of the effects of computer systems on human education can be shown as an example of this. In this paper, a reliable and real time tangible user interface system developed for the air drum game which is used both for game and computer based training programs is presented. A user can perform a play action in six different directions using electronic drum sticks. A developed algorithm detects the direction of moving stick by processing sensor data taken from these sticks. Information about the stick which is moved and its direction are sent to the sound system to play the sound file of the relevant drum instrument. In this way the user is presented with a realistic drum experience. The developed system has been tested by different users in real world and its performance has been reported. The results verify the reliability and usability of the electronic drum game.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121291789","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960311
T. Uğur, A. Erdamar
Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83 % and 85.29%, respectively.
{"title":"Detection of K-complexes in sleep EEG with support vector machines","authors":"T. Uğur, A. Erdamar","doi":"10.1109/SIU.2017.7960311","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960311","url":null,"abstract":"Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83 % and 85.29%, respectively.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116255333","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960559
Burak Çatalbaş, Bahadır Çatalbaş, Ö. Morgül
Human Activity Recognition is a popular topic of research, with the importance it carries and its limited feature vector, to reach high success rates because of the difficulty faced in classification. With the increase of movement measurability for individuals via inertia measuring units embedded inside the smartphones, the data amount increases which lets new classifiers to be designed with higher success in this field. Artificial neural networks can perform better at such classification problems in comparison to conventional classifiers. In this work, various artificial neural networks have been tried to form a classifier for the University of California (UCI) Human Activity Recognition dataset and resulting success rates for those classifiers are compared with existing results for same dataset in the literature.
{"title":"Human activity recognition with different artificial neural network based classifiers","authors":"Burak Çatalbaş, Bahadır Çatalbaş, Ö. Morgül","doi":"10.1109/SIU.2017.7960559","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960559","url":null,"abstract":"Human Activity Recognition is a popular topic of research, with the importance it carries and its limited feature vector, to reach high success rates because of the difficulty faced in classification. With the increase of movement measurability for individuals via inertia measuring units embedded inside the smartphones, the data amount increases which lets new classifiers to be designed with higher success in this field. Artificial neural networks can perform better at such classification problems in comparison to conventional classifiers. In this work, various artificial neural networks have been tried to form a classifier for the University of California (UCI) Human Activity Recognition dataset and resulting success rates for those classifiers are compared with existing results for same dataset in the literature.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590607","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960500
Baris Yamansavascilar, A. C. Baktir, Atay Ozgovde, Cem Ersoy
Recent developments in Internet technology have led to an increased importance of Software-Defined Networks (SDN). Due to advantages of this new network model that controls the network centrally, many service providers and vendors expect that traditional networks should be superseded by SDN. However, because of their centralized nature, they are vulnerable in terms of reliability and fault-tolerance issues both on data and control planes. Thus, developing such a fault-tolerant SDN design is quite important. In this study, fault tolerance on the data plane is targeted by considering various network and performance measurements. In the experiments, the impact of the topology size, frequency of packets, and the number of flows in the current route on the recovery time is tested. Moreover, local and global recovery approaches are compared.
{"title":"Fault tolerant data plane using SDN","authors":"Baris Yamansavascilar, A. C. Baktir, Atay Ozgovde, Cem Ersoy","doi":"10.1109/SIU.2017.7960500","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960500","url":null,"abstract":"Recent developments in Internet technology have led to an increased importance of Software-Defined Networks (SDN). Due to advantages of this new network model that controls the network centrally, many service providers and vendors expect that traditional networks should be superseded by SDN. However, because of their centralized nature, they are vulnerable in terms of reliability and fault-tolerance issues both on data and control planes. Thus, developing such a fault-tolerant SDN design is quite important. In this study, fault tolerance on the data plane is targeted by considering various network and performance measurements. In the experiments, the impact of the topology size, frequency of packets, and the number of flows in the current route on the recovery time is tested. Moreover, local and global recovery approaches are compared.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130942472","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960373
Mustafa Ustuner, F. B. Sanli, G. Bilgin, S. Abdikan
In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.
{"title":"Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul","authors":"Mustafa Ustuner, F. B. Sanli, G. Bilgin, S. Abdikan","doi":"10.1109/SIU.2017.7960373","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960373","url":null,"abstract":"In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133420942","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 : 2017-05-15DOI: 10.1109/SIU.2017.7960216
S. Karasu, Z. Saraç
In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by Sequential Forward Selection (SFS) method. The performance of models with different hidden layer neuron numbers tested at different noise levels (40 dB, 30 dB and 20 dB) by using the selected attributes. In this study, it was found that for the most appropriate number of attributes and optimal model parameters, performance in noisy environment (20 dB) and overall performance were 99.0%.
{"title":"Classification of power quality disturbances with S-transform and artificial neural networks method","authors":"S. Karasu, Z. Saraç","doi":"10.1109/SIU.2017.7960216","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960216","url":null,"abstract":"In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by Sequential Forward Selection (SFS) method. The performance of models with different hidden layer neuron numbers tested at different noise levels (40 dB, 30 dB and 20 dB) by using the selected attributes. In this study, it was found that for the most appropriate number of attributes and optimal model parameters, performance in noisy environment (20 dB) and overall performance were 99.0%.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115442717","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：