This paper investigates the effect of feature parameters on the objective assessment of stress under indoor noise conditions. To this end, both a qualitative stress parameter and quantitative feature parameters are collected according to different sequences of indoor noises. In other words, the qualitative stress parameter is obtained by continuously logging the perceptual intensity of stress for a certain period with a score ranging from 1 to 10 under indoor noise conditions. In addition, the quantitative feature parameters are represented by the combination of various audio feature parameters consisting of psychoacoustic, spectral, and temporal features. Finally, multiple linear regression analysis is conducted on the pairs of the qualitative stress parameter and possible combinations of quantitative feature parameters. It is shown from the evaluation that combinations of feature parameters could have a coefficient of determination of up to 0.9607.
{"title":"Analysis of Feature Parameters for Objective Stress Assessment of Indoor Noises","authors":"Kwang Myung Jeon, H. Kim","doi":"10.1109/AIMS.2015.64","DOIUrl":"https://doi.org/10.1109/AIMS.2015.64","url":null,"abstract":"This paper investigates the effect of feature parameters on the objective assessment of stress under indoor noise conditions. To this end, both a qualitative stress parameter and quantitative feature parameters are collected according to different sequences of indoor noises. In other words, the qualitative stress parameter is obtained by continuously logging the perceptual intensity of stress for a certain period with a score ranging from 1 to 10 under indoor noise conditions. In addition, the quantitative feature parameters are represented by the combination of various audio feature parameters consisting of psychoacoustic, spectral, and temporal features. Finally, multiple linear regression analysis is conducted on the pairs of the qualitative stress parameter and possible combinations of quantitative feature parameters. It is shown from the evaluation that combinations of feature parameters could have a coefficient of determination of up to 0.9607.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121256683","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}
The idea of particle swarm optimization falls under the domain of swarm intelligence. Particle swarm optimization technique is widely used for finding the global minima of well-known benchmark functions. The main idea behind this technique is that working in a group improves the performance of a system. A modified particle swarm optimization technique is proposed in this paper and tested on seven standard benchmark functions. The two major modifications are introduced in the standard particle swarm optimization, modify the velocity of a particle such that the particle remains within the confine limits of clamp velocity, and penalize the particle velocity, if the sum of the velocity vector and position vector results in breaching the boundary limits of search space. The results of the modified PSO are compared with the two versions of standard PSO, constant inertial weight with no velocity clamping and linearly decreasing inertial weight with no velocity clamping.
{"title":"Improved Particle Swarm Optimization Based on Velocity Clamping and Particle Penalization","authors":"Musaed A. Alhussein, Syed Irtaza Haider","doi":"10.1109/AIMS.2015.20","DOIUrl":"https://doi.org/10.1109/AIMS.2015.20","url":null,"abstract":"The idea of particle swarm optimization falls under the domain of swarm intelligence. Particle swarm optimization technique is widely used for finding the global minima of well-known benchmark functions. The main idea behind this technique is that working in a group improves the performance of a system. A modified particle swarm optimization technique is proposed in this paper and tested on seven standard benchmark functions. The two major modifications are introduced in the standard particle swarm optimization, modify the velocity of a particle such that the particle remains within the confine limits of clamp velocity, and penalize the particle velocity, if the sum of the velocity vector and position vector results in breaching the boundary limits of search space. The results of the modified PSO are compared with the two versions of standard PSO, constant inertial weight with no velocity clamping and linearly decreasing inertial weight with no velocity clamping.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127879644","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}
This paper investigates insensitive H∞ control problems for linear continuous-time systems with input and measurement quantization via static output feedback (SOF). The designed controllers are insensitive to additive interval-bounded controller coefficient variations and are made up of two parts as linear and nonlinear parts. The former is used to achieve the H∞ performance against external disturbances, unknown initial states and controller coefficient variations. The latter is used to attenuate the effect of input and measurement quantization simultaneously. It should be mentioned that three novel linear matrix inequality (LMI) conditions by using three different methods without introducing a matrix equality constraint as many existing results are obtained. Finally, a numerical example is presented to show the effectiveness and advantages of the proposed methods.
{"title":"Quantized H[infinity] Static Output Control for Linear Systems with Interval-Bounded Additive Controller Coefficient Variations","authors":"Xiang-Gui Guo, Jianliang Wang, F. Liao","doi":"10.1109/AIMS.2015.45","DOIUrl":"https://doi.org/10.1109/AIMS.2015.45","url":null,"abstract":"This paper investigates insensitive H∞ control problems for linear continuous-time systems with input and measurement quantization via static output feedback (SOF). The designed controllers are insensitive to additive interval-bounded controller coefficient variations and are made up of two parts as linear and nonlinear parts. The former is used to achieve the H∞ performance against external disturbances, unknown initial states and controller coefficient variations. The latter is used to attenuate the effect of input and measurement quantization simultaneously. It should be mentioned that three novel linear matrix inequality (LMI) conditions by using three different methods without introducing a matrix equality constraint as many existing results are obtained. Finally, a numerical example is presented to show the effectiveness and advantages of the proposed methods.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132652942","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}
Feature selection is the process of choosing a subset of the available features or attributes from a certain dataset in order to render the process of building a predictive model more efficient and accurate. The selection of attributes is, in most of the times, done sequentially. In this paper we propose a new filtering strategy that selects the attributes in a composite way rather than sequential. The advantage of this approach is that it allows for an important number of features that are highly relevant to their classes but statistically insignificant to participate in the learning process of the classifier. Results show that this new approach is promising and as good as the traditional one. Higher accuracy is reached when the number of the infrequent features increases. This approach is useful when we need for the infrequent features to be part of the predictive model since this, in turn, enforces the subjectivity of the decision made by the classifier.
{"title":"Dimensionality Reduction with a Composite-Selective Strategy in Documents with a Hybrid Content","authors":"S. Raheel","doi":"10.1109/AIMS.2015.28","DOIUrl":"https://doi.org/10.1109/AIMS.2015.28","url":null,"abstract":"Feature selection is the process of choosing a subset of the available features or attributes from a certain dataset in order to render the process of building a predictive model more efficient and accurate. The selection of attributes is, in most of the times, done sequentially. In this paper we propose a new filtering strategy that selects the attributes in a composite way rather than sequential. The advantage of this approach is that it allows for an important number of features that are highly relevant to their classes but statistically insignificant to participate in the learning process of the classifier. Results show that this new approach is promising and as good as the traditional one. Higher accuracy is reached when the number of the infrequent features increases. This approach is useful when we need for the infrequent features to be part of the predictive model since this, in turn, enforces the subjectivity of the decision made by the classifier.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129235491","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}
Yoshiaki Morino, T. Hiraguri, H. Yoshino, K. Nishimori, Atsuo Tachibana, T. Matsuda
In IEEE 802.11[1] wireless LANs based onCSMA/CA (Carrier Sense Multiple Access with CollisionAvoidance), parameters such as contention window (CW)significantly affects its throughput performance. In this paper,we propose a novel CW control scheme in order to achievethe high throughput performance in dense user environments.While the standard CSMA/CA mechanism employs an adaptiveCW control according to the number of packet retransmissions,the proposed scheme uses the optimum CW size, which is afunction of the number of terminal stations (STAs). In theproposed scheme, an access points (AP) estimates the numberof STAs from the measured packet collision probability, andderives the optimum CW size based on a theoretical analysisusing a Markov chain model. With simulation experimentsin a dense environment, we evaluate the performance of theproposed scheme and show that it significantly improves thethroughput performance.
在基于csma /CA (Carrier Sense Multiple Access with CollisionAvoidance)的IEEE 802.11[1]无线局域网中，争用窗口(CW)等参数对其吞吐量性能影响很大。在本文中，我们提出了一种新的连续波控制方案，以实现在密集用户环境下的高吞吐量性能。标准的CSMA/CA机制根据分组重传的数量采用自适应的CW控制，而本方案采用的CW大小是终端站(sta)数量的函数。在该方案中，接入点(AP)根据测量的分组碰撞概率估计sta的数量，并基于马尔可夫链模型的理论分析得出最佳CW大小。通过在密集环境下的仿真实验，我们评估了该方案的性能，并表明该方案显著提高了吞吐量性能。
{"title":"A Novel Contention Window Control Scheme Based on a Markov Chain Model in Dense WLAN Environment","authors":"Yoshiaki Morino, T. Hiraguri, H. Yoshino, K. Nishimori, Atsuo Tachibana, T. Matsuda","doi":"10.1109/AIMS.2015.72","DOIUrl":"https://doi.org/10.1109/AIMS.2015.72","url":null,"abstract":"In IEEE 802.11[1] wireless LANs based onCSMA/CA (Carrier Sense Multiple Access with CollisionAvoidance), parameters such as contention window (CW)significantly affects its throughput performance. In this paper,we propose a novel CW control scheme in order to achievethe high throughput performance in dense user environments.While the standard CSMA/CA mechanism employs an adaptiveCW control according to the number of packet retransmissions,the proposed scheme uses the optimum CW size, which is afunction of the number of terminal stations (STAs). In theproposed scheme, an access points (AP) estimates the numberof STAs from the measured packet collision probability, andderives the optimum CW size based on a theoretical analysisusing a Markov chain model. With simulation experimentsin a dense environment, we evaluate the performance of theproposed scheme and show that it significantly improves thethroughput performance.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"658 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123354333","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}
The paper describes a novel method that improvises the procedure for supervised speaker diarization. The procedure supposes that the database of the speakers is available. Initially, the database and observation signal of the speakers, are prepared. The audio features has been extracted from the database and the observation signal. Instead of the using of one of Mel Frequency Cepstral Coefficient, Perceptual Linear Prediction, or Power Normalized Cepstral Coefficients, a combination of all of them have been used. The combination form of these features is independent, i.e. They are concatenated in the feature matrix. The comparison between features of observation signal and statistical properties of database features, has been made. The comparing procedure is used to make the decision of the logical mask of the comparison. Both of bottom-up and top-down scenarios collaborate to complete the last decisions successfully. Diarization Error Rate test denotes that combination of features has less than errors than any one alone.
{"title":"Statistical Speaker Diarization Using Dependent Combination of Extracted Features","authors":"Hasan Almgotir Kadhim, L. Woo, S. Dlay","doi":"10.1109/AIMS.2015.53","DOIUrl":"https://doi.org/10.1109/AIMS.2015.53","url":null,"abstract":"The paper describes a novel method that improvises the procedure for supervised speaker diarization. The procedure supposes that the database of the speakers is available. Initially, the database and observation signal of the speakers, are prepared. The audio features has been extracted from the database and the observation signal. Instead of the using of one of Mel Frequency Cepstral Coefficient, Perceptual Linear Prediction, or Power Normalized Cepstral Coefficients, a combination of all of them have been used. The combination form of these features is independent, i.e. They are concatenated in the feature matrix. The comparison between features of observation signal and statistical properties of database features, has been made. The comparing procedure is used to make the decision of the logical mask of the comparison. Both of bottom-up and top-down scenarios collaborate to complete the last decisions successfully. Diarization Error Rate test denotes that combination of features has less than errors than any one alone.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122645027","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}
Different sensitivity measures are investigated for YOULA-parameterized regulators and the influence of a new observer topology is treated. The paper extends the observer principle for YOULA regulators reducing the model error similar to the classical state feedback/observer topologies.
{"title":"Sensitivity Measures and Modeling Errors for YOULA Parameterization Based Regulators","authors":"C. Bányász, L. Keviczky","doi":"10.1109/AIMS.2015.36","DOIUrl":"https://doi.org/10.1109/AIMS.2015.36","url":null,"abstract":"Different sensitivity measures are investigated for YOULA-parameterized regulators and the influence of a new observer topology is treated. The paper extends the observer principle for YOULA regulators reducing the model error similar to the classical state feedback/observer topologies.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133181548","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}
The sensory acquisition of the environment is the most important task of mobile robotics, as it is the foundation for any ability that the robot shall have, later on. Sophisticated tasks often require an environment model for path planning,obstacle avoidance and many more. Furthermore, the robot needs to know where it is located within the environment to build-up, complement and update the model. Thus, besides environment perception, localization belongs to the most important tasks of mobile robot systems. Most approaches towards self-localization and mapping are very specific, either to one sensor type, or a strictly predefined set of sensors, prohibiting the use of the provided techniques on many different mobile systems (robots, cars or other moving platforms equipped with sensors). We present a general approach supporting the use of arbitrary numbers and types of sensors simultaneously. This allows to operate with a large variety of already existing systems without changing the hardware setup. Furthermore, the semantic environment model, generated by our solution, can directly be used for sophisticated and automated environment analyses.
{"title":"Semantic Environment Perception, Localization and Mapping","authors":"Bjoern Sondermann, J. Rossmann","doi":"10.1109/AIMS.2015.84","DOIUrl":"https://doi.org/10.1109/AIMS.2015.84","url":null,"abstract":"The sensory acquisition of the environment is the most important task of mobile robotics, as it is the foundation for any ability that the robot shall have, later on. Sophisticated tasks often require an environment model for path planning,obstacle avoidance and many more. Furthermore, the robot needs to know where it is located within the environment to build-up, complement and update the model. Thus, besides environment perception, localization belongs to the most important tasks of mobile robot systems. Most approaches towards self-localization and mapping are very specific, either to one sensor type, or a strictly predefined set of sensors, prohibiting the use of the provided techniques on many different mobile systems (robots, cars or other moving platforms equipped with sensors). We present a general approach supporting the use of arbitrary numbers and types of sensors simultaneously. This allows to operate with a large variety of already existing systems without changing the hardware setup. Furthermore, the semantic environment model, generated by our solution, can directly be used for sophisticated and automated environment analyses.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128277708","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}
Internet of Things (IoT) is a common thing (object) in today's world, which serves as part of our routine life activities. Although it benefits the residential district in several ways, various challenges such as data confidentiality and privacy are created. As a matter of fact, the community is concerned what information may leak out via IoT. Therefore, the needs of a secure environment is vital in order to secure the transmitting data from it devices over the network. As a result, in this paper, a secure scheme is suggested on using image steganography as an alternative security mechanism in conjunction with a home server to secure the transmitted data from IP camera as the IoT device to the other devices, either in LAN or WAN networks.
{"title":"Internet of Things: Securing Data Using Image Steganography","authors":"J. H. Yin, Gan Fen, Fiza Mughal, Vahab Iranmanesh","doi":"10.1109/AIMS.2015.56","DOIUrl":"https://doi.org/10.1109/AIMS.2015.56","url":null,"abstract":"Internet of Things (IoT) is a common thing (object) in today's world, which serves as part of our routine life activities. Although it benefits the residential district in several ways, various challenges such as data confidentiality and privacy are created. As a matter of fact, the community is concerned what information may leak out via IoT. Therefore, the needs of a secure environment is vital in order to secure the transmitting data from it devices over the network. As a result, in this paper, a secure scheme is suggested on using image steganography as an alternative security mechanism in conjunction with a home server to secure the transmitted data from IP camera as the IoT device to the other devices, either in LAN or WAN networks.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133426630","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}
Z. Yusof, I. Ibrahim, Siti Nurzulaikha Satiman, Z. Ibrahim, Nor Hidayati Abd Aziz, Nor Azlina Ab. Aziz
Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, an extended version of SKF algorithm, which is termed as Binary SKF (BSKF), is proposed. Similar to existing approach, a mapping function is used to enable the SKF algorithm to operate in binary search space. A set of traveling salesman problems are used to evaluate the performance of the proposed BSKF against Binary Gravitational Search Algorithm (BGSA) and Binary Particle Swarm Optimization (BPSO).
{"title":"BSKF: Simulated Kalman Filter","authors":"Z. Yusof, I. Ibrahim, Siti Nurzulaikha Satiman, Z. Ibrahim, Nor Hidayati Abd Aziz, Nor Azlina Ab. Aziz","doi":"10.1109/AIMS.2015.23","DOIUrl":"https://doi.org/10.1109/AIMS.2015.23","url":null,"abstract":"Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, an extended version of SKF algorithm, which is termed as Binary SKF (BSKF), is proposed. Similar to existing approach, a mapping function is used to enable the SKF algorithm to operate in binary search space. A set of traveling salesman problems are used to evaluate the performance of the proposed BSKF against Binary Gravitational Search Algorithm (BGSA) and Binary Particle Swarm Optimization (BPSO).","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124775740","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：