Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002100
Yu-ting Huang, Chen-Huan Pi, Stone Cheng
Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. We demonstrate the performance of the trained reinforcement learning controller generated trajectory information and manipulated quadrotor toward the perching point (manually throwing it up in the air with an initial velocity of 1 m/s). We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with relatively accurate. Computation time of evaluating the policy is only 0.03 sec per trajectory, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.
{"title":"Omnidirectional Autonomous Aggressive Perching of Unmanned Aerial Vehicle using Reinforcement Learning Trajectory Generation and Control","authors":"Yu-ting Huang, Chen-Huan Pi, Stone Cheng","doi":"10.1109/SCISISIS55246.2022.10002100","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002100","url":null,"abstract":"Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. We demonstrate the performance of the trained reinforcement learning controller generated trajectory information and manipulated quadrotor toward the perching point (manually throwing it up in the air with an initial velocity of 1 m/s). We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with relatively accurate. Computation time of evaluating the policy is only 0.03 sec per trajectory, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"4 1","pages":"1-6"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80351093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002126
Akio Shinohara, Takashi Izumi
We discuss how to classify an open systems problem and automated action plan determination. In the current situation where problems of open systems are a regular occurrence, there is a strong demand for automation of failure action plans. First, we propose the way how to evaluate and classify all problems that happens in open systems. Next, we provide a way to link problem classes to unique action plan. This enables automated action plan determination. Finally, we analyze the relation of wrong DOA detection and inappropriate action plan determination.
{"title":"A Discussion of Classifying Open Systems Problem and Automated Action Plan Selection","authors":"Akio Shinohara, Takashi Izumi","doi":"10.1109/SCISISIS55246.2022.10002126","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002126","url":null,"abstract":"We discuss how to classify an open systems problem and automated action plan determination. In the current situation where problems of open systems are a regular occurrence, there is a strong demand for automation of failure action plans. First, we propose the way how to evaluate and classify all problems that happens in open systems. Next, we provide a way to link problem classes to unique action plan. This enables automated action plan determination. Finally, we analyze the relation of wrong DOA detection and inappropriate action plan determination.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"70 1","pages":"1-7"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79101775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002000
Yuri Murayama, Ichiro Kobayashi
The Differentiable Neural Computer (DNC), a neural network model with an addressable external memory, can solve algorithmic and question answering tasks. As improved versions of DNC, rsDNC and DNC-DMS have been proposed. However, how to integrate structured knowledge into these DNC models remains a challenging research question. We incorporate an architecture for knowledge into such DNC models, i.e. DNC, rsDNC and DNC-DMS, to improve the ability to generate correct answers for questions using both contextual information and structured knowledge. Our improved rsDNC model outperformed the other models with the mean top-l accuracy and top-10 accuracy in GEO dataset. In addition, our improved rsDNC model achieved the best performance with the mean top-10 accuracy in augmented GEO dataset.
{"title":"Towards Question Answering with Multi-hop Reasoning over Knowledge using a Neural Network Model with External Memories","authors":"Yuri Murayama, Ichiro Kobayashi","doi":"10.1109/SCISISIS55246.2022.10002000","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002000","url":null,"abstract":"The Differentiable Neural Computer (DNC), a neural network model with an addressable external memory, can solve algorithmic and question answering tasks. As improved versions of DNC, rsDNC and DNC-DMS have been proposed. However, how to integrate structured knowledge into these DNC models remains a challenging research question. We incorporate an architecture for knowledge into such DNC models, i.e. DNC, rsDNC and DNC-DMS, to improve the ability to generate correct answers for questions using both contextual information and structured knowledge. Our improved rsDNC model outperformed the other models with the mean top-l accuracy and top-10 accuracy in GEO dataset. In addition, our improved rsDNC model achieved the best performance with the mean top-10 accuracy in augmented GEO dataset.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"37 18 1","pages":"1-6"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78335532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10001871
Tsz Chun Yeung, Cho Yin Yiu, K. Ng, Ho Sum Chu, Pui Hang Jong
As one of the busiest international airports, Hong Kong International Airport has to handle a large number of flights every day. It is essential for air traffic controllers to assign flight schedules within a short period of time. Under extreme weather, especially when tropical cyclones edge close to Hong Kong, it is of great importance to arrange the flights to perform appropriate actions such that aviation safety could be guaranteed. Thus, an accurate weather forecast is paramount under such circumstances. SARIMA model is adopted in this paper to predict wind speed for flights. A network model has also been constructed using the prediction of wind speed from the SARIMA model to minimise the time required for the flights to be landed on the runway.
{"title":"Airside terminal traffic flow problem formulation under extreme weather: A case study in the Hong Kong International Airport","authors":"Tsz Chun Yeung, Cho Yin Yiu, K. Ng, Ho Sum Chu, Pui Hang Jong","doi":"10.1109/SCISISIS55246.2022.10001871","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10001871","url":null,"abstract":"As one of the busiest international airports, Hong Kong International Airport has to handle a large number of flights every day. It is essential for air traffic controllers to assign flight schedules within a short period of time. Under extreme weather, especially when tropical cyclones edge close to Hong Kong, it is of great importance to arrange the flights to perform appropriate actions such that aviation safety could be guaranteed. Thus, an accurate weather forecast is paramount under such circumstances. SARIMA model is adopted in this paper to predict wind speed for flights. A network model has also been constructed using the prediction of wind speed from the SARIMA model to minimise the time required for the flights to be landed on the runway.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"5 1","pages":"1-6"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79167387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002120
Tomoya Asakura, Asuka Terai
This study investigated the effects from including humor stimuli in an interaction with a recommendation chatbot on user interest in a recommended item. Three types of chatbots were developed with different frequencies of humor stimuli. A psychological experiment was conducted to investigate the differences in user interest in the recommended item. As a result, no significant direct effect on user interest was observed depending on the frequency of the humor stimuli. Nevertheless, the sense of humor, trust, and humanity had effects on the user interest. Moreover, the results suggested that humor stimuli had a positive effect on sense of humor and negative effect on trust.
{"title":"Effect of Humor on User Interest in a Recommendation Chatbot","authors":"Tomoya Asakura, Asuka Terai","doi":"10.1109/SCISISIS55246.2022.10002120","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002120","url":null,"abstract":"This study investigated the effects from including humor stimuli in an interaction with a recommendation chatbot on user interest in a recommended item. Three types of chatbots were developed with different frequencies of humor stimuli. A psychological experiment was conducted to investigate the differences in user interest in the recommended item. As a result, no significant direct effect on user interest was observed depending on the frequency of the humor stimuli. Nevertheless, the sense of humor, trust, and humanity had effects on the user interest. Moreover, the results suggested that humor stimuli had a positive effect on sense of humor and negative effect on trust.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"2 1","pages":"1-4"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86349677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recent coronavirus disease 2019 (COVID-19) outbreak has helped increase the popularity of online video communication and meeting platforms as alternatives to face-to-face interactions. Such a trend has also triggered the emergence of remote cheering systems, hinting at the possibility that people could enjoy watching sports games in virtual environments even from their homes in the near future. However, reproducing a sense of presence similar to the atmosphere felt by fans in stadiums or event venues is a major challenge within virtual environments. Thus, our idea is to embed groups of cheerful robots in virtual environments, thereby creating a sense of unity and mimicking emotion spread among fans. In this study, we built a virtual cheering environment and embedded a game-event driven behavior model that enables a group of robots to display various emotions through nonverbal reactions according to the game flow. Then, we conducted a preliminary evaluation of the proposed system, where the participants and a group of robots were placed in a virtual cheering environment to watch a baseball game. The obtained results hinted at the meaningfulness of the proposed approach. Nevertheless, further work is necessary to achieve a sufficient sense of presence and validate the effectiveness of our proposed environment.
{"title":"Human-robot interaction environment to enhance the sense of presence in remote sports watching","authors":"Fuma Yamamoto, Emmanuel Ayedoun, Masataka Tokumaru","doi":"10.1109/SCISISIS55246.2022.10002053","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002053","url":null,"abstract":"The recent coronavirus disease 2019 (COVID-19) outbreak has helped increase the popularity of online video communication and meeting platforms as alternatives to face-to-face interactions. Such a trend has also triggered the emergence of remote cheering systems, hinting at the possibility that people could enjoy watching sports games in virtual environments even from their homes in the near future. However, reproducing a sense of presence similar to the atmosphere felt by fans in stadiums or event venues is a major challenge within virtual environments. Thus, our idea is to embed groups of cheerful robots in virtual environments, thereby creating a sense of unity and mimicking emotion spread among fans. In this study, we built a virtual cheering environment and embedded a game-event driven behavior model that enables a group of robots to display various emotions through nonverbal reactions according to the game flow. Then, we conducted a preliminary evaluation of the proposed system, where the participants and a group of robots were placed in a virtual cheering environment to watch a baseball game. The obtained results hinted at the meaningfulness of the proposed approach. Nevertheless, further work is necessary to achieve a sufficient sense of presence and validate the effectiveness of our proposed environment.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"116 1","pages":"1-5"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80673997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002056
Shin Beom Hur, Keon Myung Lee
There are some feature image coding techniques to convert a time series into an image which represents temporal characteristics into spatial information. Convolutional neural network (CNN) based models have been developed for image-coded time series data classification. This paper proposes an MLP-Mixer based model for time series data classification. The proposed model has been compared to a CNN-based model in terms of their image coding and the number of parameters. In the experiments, with fewer parameters, the proposed MLP-Mixer based method has shown comparable performance to the CNN-based model. It also showed that the different combinations of feature image coding could enhance the performance of the classification model.
{"title":"Image-Coded Time Series Classification with MLP-Mixer","authors":"Shin Beom Hur, Keon Myung Lee","doi":"10.1109/SCISISIS55246.2022.10002056","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002056","url":null,"abstract":"There are some feature image coding techniques to convert a time series into an image which represents temporal characteristics into spatial information. Convolutional neural network (CNN) based models have been developed for image-coded time series data classification. This paper proposes an MLP-Mixer based model for time series data classification. The proposed model has been compared to a CNN-based model in terms of their image coding and the number of parameters. In the experiments, with fewer parameters, the proposed MLP-Mixer based method has shown comparable performance to the CNN-based model. It also showed that the different combinations of feature image coding could enhance the performance of the classification model.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"21 1","pages":"1-2"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83813840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10001888
Batuhan Durukal, Mert Eren, Ömer Çetin, Egemen Karabıyık, Namik Zengin, Sarp Kaya Yetkin
Subjective evaluation plays a key role in autonomous driving feature validation for safety, comfort, and driving quality. As for being objective, it is also important to evaluate the autonomous features with key performance indicators (KPI) depending on physical parameters before stepping into the delivery phase. To provide better driving experience for autonomous features, calibration parameters need to be tuned carefully while considering safety and comfort. Calibration parameters can be evaluated in terms of safe, unsafe, comfortable, or uncomfortable states through questions that allow the evaluation of the passenger’s feelings during real-world testing which includes predefined scenarios and environments. In this paper, we proposed a method that performs the rating of the free lane change maneuver in terms of safety and comfort by employing the machine learning algorithms to model the passenger feedback according to the questionnaire for the subjective evaluation of the test maneuver execution. After trying several machine and deep learning regression techniques, we have shown that Extreme Gradient Boosting (XGB) regressor can be used to model drive feeling accurately for validation and calibration purposes. The constituted evaluation model can be utilized to improve quality of the autonomous driving, optimize calibration parameters and achieve user acceptance.
{"title":"How Can Machine Learning Models Be Used for Subjective Assessment of Safety and Comfort: Application on Free Lane Change Maneuver","authors":"Batuhan Durukal, Mert Eren, Ömer Çetin, Egemen Karabıyık, Namik Zengin, Sarp Kaya Yetkin","doi":"10.1109/SCISISIS55246.2022.10001888","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10001888","url":null,"abstract":"Subjective evaluation plays a key role in autonomous driving feature validation for safety, comfort, and driving quality. As for being objective, it is also important to evaluate the autonomous features with key performance indicators (KPI) depending on physical parameters before stepping into the delivery phase. To provide better driving experience for autonomous features, calibration parameters need to be tuned carefully while considering safety and comfort. Calibration parameters can be evaluated in terms of safe, unsafe, comfortable, or uncomfortable states through questions that allow the evaluation of the passenger’s feelings during real-world testing which includes predefined scenarios and environments. In this paper, we proposed a method that performs the rating of the free lane change maneuver in terms of safety and comfort by employing the machine learning algorithms to model the passenger feedback according to the questionnaire for the subjective evaluation of the test maneuver execution. After trying several machine and deep learning regression techniques, we have shown that Extreme Gradient Boosting (XGB) regressor can be used to model drive feeling accurately for validation and calibration purposes. The constituted evaluation model can be utilized to improve quality of the autonomous driving, optimize calibration parameters and achieve user acceptance.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"64 1","pages":"1-6"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81454800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10001858
Maximiliano Wakugawa, Fumiaki Saitoh
Various simulations are currently being conducted in response to the spread of the novel coronavirus infection. However, few multi-agent simulations have been conducted using a model that considers asymptomatic persons, who are one of the factors contributing to the spread of infection. In this study, we extended the SEAIR model, which considers asymptomatic persons, to multi-agent simulations to investigate the effect of the proportion of asymptomatic persons on the effective number of reproductions. The results indicate that asymptomatic persons may influence the number of positive groups at the peak of the spread of infection and the convergence period.
{"title":"Impact of COVID-19 asymptomatic individuals on effective regenerative math by multi-agent simulation based on the SEAIR model.","authors":"Maximiliano Wakugawa, Fumiaki Saitoh","doi":"10.1109/SCISISIS55246.2022.10001858","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10001858","url":null,"abstract":"Various simulations are currently being conducted in response to the spread of the novel coronavirus infection. However, few multi-agent simulations have been conducted using a model that considers asymptomatic persons, who are one of the factors contributing to the spread of infection. In this study, we extended the SEAIR model, which considers asymptomatic persons, to multi-agent simulations to investigate the effect of the proportion of asymptomatic persons on the effective number of reproductions. The results indicate that asymptomatic persons may influence the number of positive groups at the peak of the spread of infection and the convergence period.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"49 1","pages":"1-4"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85270785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.1109/SCISISIS55246.2022.10002097
Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jin Yong Kim, Baekcheon Kim, Sungshin Kim
Tool diagnosis system is necessary to prevent an accident or defective product. This paper proposes the tool diagnosis method of CNC machines based on color space conversion and deep learning. To apply the deep learning algorithm, we generated images from the current data of CNC machines by wavelet transform. However, generated images by wavelet transform are difficult to distinguish whether it is normal data image or not. Because generated images by wavelet transform are very similar and there is no outstanding feature. Therefore, we applied color space conversion from RGB image to CIE L*a*b* image. Converted images represent outstanding features whereas generated images by wavelet transform and RGB images do not. And, to make up for imbalanced data, oversampling is applied. Finally, deep learning algorithm is trained to classify the converted images. Experimental results showed that the proposed method can implement the deep learning network for tool diagnosis of CNC machine effectively.
{"title":"Tool Diagnosis Method of CNC Machine based on Color Space Conversion and Deep Learning","authors":"Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jin Yong Kim, Baekcheon Kim, Sungshin Kim","doi":"10.1109/SCISISIS55246.2022.10002097","DOIUrl":"https://doi.org/10.1109/SCISISIS55246.2022.10002097","url":null,"abstract":"Tool diagnosis system is necessary to prevent an accident or defective product. This paper proposes the tool diagnosis method of CNC machines based on color space conversion and deep learning. To apply the deep learning algorithm, we generated images from the current data of CNC machines by wavelet transform. However, generated images by wavelet transform are difficult to distinguish whether it is normal data image or not. Because generated images by wavelet transform are very similar and there is no outstanding feature. Therefore, we applied color space conversion from RGB image to CIE L*a*b* image. Converted images represent outstanding features whereas generated images by wavelet transform and RGB images do not. And, to make up for imbalanced data, oversampling is applied. Finally, deep learning algorithm is trained to classify the converted images. Experimental results showed that the proposed method can implement the deep learning network for tool diagnosis of CNC machine effectively.","PeriodicalId":21408,"journal":{"name":"Rice","volume":"40 1","pages":"1-4"},"PeriodicalIF":5.5,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84539880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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