Pub Date : 2020-10-11DOI: 10.1109/SMC42975.2020.9283080
Dongyuan Wang, F. Qiao, Junkai Wang, Juan Liu, Weichang Kong
With the increasing demand of personalized products and the application of emerging technologies, substantial unexpected events appears in smart factories. Machine learning based adaptive scheduling shows significant appeal in smart shop floors, yet still has limitations in accommodating unexpected events. This paper presents a novel framework of HCPS (Human Cyber Physical System) based on the conventional CPS. A human-machine cooperative mechanism is proposed to coordinate task allocation between human and machine. Meanwhile, in order to integrate human intelligence and machine intelligence within scheduling decision making, a novel human-machine cooperative approach for adaptive scheduling is put forward. In the process of online scheduling, human operators adjust the deviation of production indicators on the basis of current condition. Subsequently, an enhanced fuzzy inference system combining with human intelligence is designed to obtain optimal dispatching rules, in which parameters are reduced by a K-means algorithm and optimized by a PSO algorithm. Finally, a case study is performed on the Minifab model. The simulation results validate the superiority of the proposed framework and approaches, and show good potential in efficiency and stability.
{"title":"Human-Machine Cooperation Based Adaptive Scheduling for a Smart Shop Floor","authors":"Dongyuan Wang, F. Qiao, Junkai Wang, Juan Liu, Weichang Kong","doi":"10.1109/SMC42975.2020.9283080","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283080","url":null,"abstract":"With the increasing demand of personalized products and the application of emerging technologies, substantial unexpected events appears in smart factories. Machine learning based adaptive scheduling shows significant appeal in smart shop floors, yet still has limitations in accommodating unexpected events. This paper presents a novel framework of HCPS (Human Cyber Physical System) based on the conventional CPS. A human-machine cooperative mechanism is proposed to coordinate task allocation between human and machine. Meanwhile, in order to integrate human intelligence and machine intelligence within scheduling decision making, a novel human-machine cooperative approach for adaptive scheduling is put forward. In the process of online scheduling, human operators adjust the deviation of production indicators on the basis of current condition. Subsequently, an enhanced fuzzy inference system combining with human intelligence is designed to obtain optimal dispatching rules, in which parameters are reduced by a K-means algorithm and optimized by a PSO algorithm. Finally, a case study is performed on the Minifab model. The simulation results validate the superiority of the proposed framework and approaches, and show good potential in efficiency and stability.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"30 1","pages":"788-793"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83864930","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9282975
Jonathan Carrero, Ismael Rodríguez, F. Rubio
When some resources are to be distributed among a set of agents following egalitarian social welfare, the goal is to maximize the utility of the agent whose utility turns out to be minimal. In this context, agents can have an incentive to lie about their actual preferences, so that more valuable resources are assigned to them. In this paper we analyze this situation, and we present a practical study where genetic algorithms are used to assess the benefits of lying under different situations.
{"title":"Measuring the benefits of lying in MARA under egalitarian social welfare","authors":"Jonathan Carrero, Ismael Rodríguez, F. Rubio","doi":"10.1109/SMC42975.2020.9282975","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282975","url":null,"abstract":"When some resources are to be distributed among a set of agents following egalitarian social welfare, the goal is to maximize the utility of the agent whose utility turns out to be minimal. In this context, agents can have an incentive to lie about their actual preferences, so that more valuable resources are assigned to them. In this paper we analyze this situation, and we present a practical study where genetic algorithms are used to assess the benefits of lying under different situations.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"41 1","pages":"559-566"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82853352","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9283435
M. Islam, Afshin Rahimi
Satellites are complicated systems, and there are many interconnected devices inside a satellite that needs to be healthy to ensure the proper functionality of a satellite. Uncertainty and mechanical failure in different integral parts of the satellite pose the major threat for the satellite to remain fully functional for its expected life span. One of the most common reasons for satellite failure is the reaction wheel (RW) failure. Satellite RW fault prognosis can be formed as a two-step process. In this paper, we study the first step where a data-driven approach is used for forecasting the RW parameters that can be used to predict the remaining useful life (RUL) of a reaction wheel onboard satellite. Autoregressive integrated moving average model (ARIMA) and a type of recurrent neural network (RNN) known as the long short-term memory (LSTM) are used for time-series forecasting in this paper. Both models can predict up to a degree of accuracy, even when limited historical data is available. ARIMA works very efficiently as it can capture a suite of different standard temporal structures in time series. Still, when it comes to accuracy, LSTM provides a better regression outcome for our dataset. The results obtained by the models are very optimistic when model parameters are tuned.
{"title":"Use of A Data-Driven Approach for Time Series Prediction in Fault Prognosis of Satellite Reaction Wheel","authors":"M. Islam, Afshin Rahimi","doi":"10.1109/SMC42975.2020.9283435","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283435","url":null,"abstract":"Satellites are complicated systems, and there are many interconnected devices inside a satellite that needs to be healthy to ensure the proper functionality of a satellite. Uncertainty and mechanical failure in different integral parts of the satellite pose the major threat for the satellite to remain fully functional for its expected life span. One of the most common reasons for satellite failure is the reaction wheel (RW) failure. Satellite RW fault prognosis can be formed as a two-step process. In this paper, we study the first step where a data-driven approach is used for forecasting the RW parameters that can be used to predict the remaining useful life (RUL) of a reaction wheel onboard satellite. Autoregressive integrated moving average model (ARIMA) and a type of recurrent neural network (RNN) known as the long short-term memory (LSTM) are used for time-series forecasting in this paper. Both models can predict up to a degree of accuracy, even when limited historical data is available. ARIMA works very efficiently as it can capture a suite of different standard temporal structures in time series. Still, when it comes to accuracy, LSTM provides a better regression outcome for our dataset. The results obtained by the models are very optimistic when model parameters are tuned.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"545 1","pages":"3624-3628"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89005453","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9282846
Yohen Thounaojam, Wiliam Setiawan, Apurva Narayan
Time-sensitive safety-critical systems store traces as a collection of time-stamped messages that are generated while a system is operating. Analysis of these traces becomes a key task as it allows one to find faults or errors within a system that is otherwise difficult to discern, especially in complex systems. Furthermore, finding any form of anomalous behaviour becomes critical in time-sensitive and safety-critical systems where a late detection will often lead to dire consequences. Most available approaches are generally used in networking or business process analysis. We focus on creating a lightweight and explainable approach for time-sensitive safety-critical systems.By using a set of system traces under both normal and anomalous conditions, our approach attempts to classify whether or not a trace is anomalous. In this work, we introduce MA2DF, Multi-Agent Anomaly Detection Framework, a novel multi-agent based graph design approach for online and offline anomaly detection in system traces. Our approach takes advantage of the timing information between a sequence of events and also the event sequences to learn and discern between normal and anomalous traces. We present two approaches, an offline approach to discern anomalous behaviour by utilizing the event occurrence workflow graph. The second approach is an online streaming algorithm that monitors the sequence of events as they arrive in real-time. This can be used to detect anomalies, find the cause, and improve system resilience. We show how our approach, MA2DF, is superior to other state-of-the-art models. The paper will explore the technical feasibility and viability of MA2DF by utilizing industry strength case study using traces from a field-tested hexacopter.
{"title":"MA2DF: A Multi-Agent Anomaly Detection Framework","authors":"Yohen Thounaojam, Wiliam Setiawan, Apurva Narayan","doi":"10.1109/SMC42975.2020.9282846","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282846","url":null,"abstract":"Time-sensitive safety-critical systems store traces as a collection of time-stamped messages that are generated while a system is operating. Analysis of these traces becomes a key task as it allows one to find faults or errors within a system that is otherwise difficult to discern, especially in complex systems. Furthermore, finding any form of anomalous behaviour becomes critical in time-sensitive and safety-critical systems where a late detection will often lead to dire consequences. Most available approaches are generally used in networking or business process analysis. We focus on creating a lightweight and explainable approach for time-sensitive safety-critical systems.By using a set of system traces under both normal and anomalous conditions, our approach attempts to classify whether or not a trace is anomalous. In this work, we introduce MA2DF, Multi-Agent Anomaly Detection Framework, a novel multi-agent based graph design approach for online and offline anomaly detection in system traces. Our approach takes advantage of the timing information between a sequence of events and also the event sequences to learn and discern between normal and anomalous traces. We present two approaches, an offline approach to discern anomalous behaviour by utilizing the event occurrence workflow graph. The second approach is an online streaming algorithm that monitors the sequence of events as they arrive in real-time. This can be used to detect anomalies, find the cause, and improve system resilience. We show how our approach, MA2DF, is superior to other state-of-the-art models. The paper will explore the technical feasibility and viability of MA2DF by utilizing industry strength case study using traces from a field-tested hexacopter.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"51 1","pages":"30-36"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89070787","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9283258
Jun-Chuan Chen, Min Cao, Zhi-hui Zhan, Dong Liu, Jun Zhang
Genetic algorithm (GA) is a widely used probabilistic search optimization algorithm. In the GA, selection is an important operator to guarantee the quality of solution. Therefore, the behavior of selection operator makes a great effect on the performance of the algorithm. This paper designs a new and efficient selection operator for GA base on the idea of promotion competition. This operator simulates the rule and process of promotion competition to protect the well perform chromosomes and eliminates poor chromosomes. This is a fundamental but significant research issue in GA that may be adopted into any existing GA variants to replace any other selection operators. We design four types of experiments to comprehensively verify the behavior of the proposed promotion selection operator, by comparing it with five other existing and commonly used selection operators. The results show that promotion selection operator has a general good performance in enhancing GA in terms of solution quality, convergence speed, and running time.
{"title":"A New and Efficient Genetic Algorithm with Promotion Selection Operator","authors":"Jun-Chuan Chen, Min Cao, Zhi-hui Zhan, Dong Liu, Jun Zhang","doi":"10.1109/SMC42975.2020.9283258","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283258","url":null,"abstract":"Genetic algorithm (GA) is a widely used probabilistic search optimization algorithm. In the GA, selection is an important operator to guarantee the quality of solution. Therefore, the behavior of selection operator makes a great effect on the performance of the algorithm. This paper designs a new and efficient selection operator for GA base on the idea of promotion competition. This operator simulates the rule and process of promotion competition to protect the well perform chromosomes and eliminates poor chromosomes. This is a fundamental but significant research issue in GA that may be adopted into any existing GA variants to replace any other selection operators. We design four types of experiments to comprehensively verify the behavior of the proposed promotion selection operator, by comparing it with five other existing and commonly used selection operators. The results show that promotion selection operator has a general good performance in enhancing GA in terms of solution quality, convergence speed, and running time.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"130 1","pages":"1532-1537"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89112670","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}
Design, control and evaluation of a lower limb wearable robotic exoskeleton for power assistance are presented in the paper. The proposed four degree-of-freedom robotic exoskeleton, an active flexion/extension and a passive abduction/adduction rotation at each hip joint, is characterized with complying with the swinging motion of lower limbs as close as possible. To perform power assistance on walking, the linear extended state observer (LESO) based controllers were designed for the walking assistance. Finally, the experiments were conducted to validate the prototype of lower limb robotic exoskeleton. The associated evaluations for the walking assistance were also investigated using the motion captured system and EMG signal.
{"title":"Design and Evaluation of a Wearable Lower Limb Robotic Exoskeleton for Power Assistance","authors":"Shi-Heng Hsu, Chuan Changcheng, Chun-Ta Chen, Yu-Cheng Wu, Wei-Yuan Lian, Tse-Min Li, C. Huang","doi":"10.1109/SMC42975.2020.9283437","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283437","url":null,"abstract":"Design, control and evaluation of a lower limb wearable robotic exoskeleton for power assistance are presented in the paper. The proposed four degree-of-freedom robotic exoskeleton, an active flexion/extension and a passive abduction/adduction rotation at each hip joint, is characterized with complying with the swinging motion of lower limbs as close as possible. To perform power assistance on walking, the linear extended state observer (LESO) based controllers were designed for the walking assistance. Finally, the experiments were conducted to validate the prototype of lower limb robotic exoskeleton. The associated evaluations for the walking assistance were also investigated using the motion captured system and EMG signal.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"29 1","pages":"2465-2470"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90140126","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9282964
Meiji Cui, Li Li, Mengchu Zhou
Many ever-increasingly complex engineering optimization problems fall into the class of High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time-consuming. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. In this paper, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is proposed for the first time. As an efficient dimension reduction tool, an autoencoder is used to compress high-dimensional landscape to informative low-dimensional space. The search operation in this low-dimensional space can facilitate the population converge towards the optima more efficiently. To balance the exploration and exploitation ability during optimization, two sub-populations coevolve in a distributed fashion, where one is assisted by an autoencoder and the other undergoes a regular evolutionary process. The information between these two sub-populations are dynamically exchanged. The proposed algorithm is validated by testing several 200 dimensional benchmark functions. Compared with the state-of-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems.
{"title":"An Autoencoder-embedded Evolutionary Optimization Framework for High-dimensional Problems","authors":"Meiji Cui, Li Li, Mengchu Zhou","doi":"10.1109/SMC42975.2020.9282964","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282964","url":null,"abstract":"Many ever-increasingly complex engineering optimization problems fall into the class of High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time-consuming. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. In this paper, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is proposed for the first time. As an efficient dimension reduction tool, an autoencoder is used to compress high-dimensional landscape to informative low-dimensional space. The search operation in this low-dimensional space can facilitate the population converge towards the optima more efficiently. To balance the exploration and exploitation ability during optimization, two sub-populations coevolve in a distributed fashion, where one is assisted by an autoencoder and the other undergoes a regular evolutionary process. The information between these two sub-populations are dynamically exchanged. The proposed algorithm is validated by testing several 200 dimensional benchmark functions. Compared with the state-of-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"11 1","pages":"1046-1051"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91382218","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9283495
Yifan Dai, Liangyu Chen, Zihan Zhang
Accurate and efficient measurements for evaluating the similarity between mathematical formulae play an important role in mathematical information retrieval. Most previous studies have focused on representing formulae in different types to catch their features and combining the traditional structure matching algorithms. This paper presents a new unsupervised model called N-ary Tree-based Formula Embedding Model (NTFEM) for the task of mathematical similarity evaluation. Using an n-ary tree structure to represent the formula, we convert the formula into a linear sequence that can be viewed as the input sentence and then embed the formula by using a word embedding model. Based on the characteristics of mathematical formulae, a weighting function is also used to get the final weighted average embedding vector. Through some experiments on NTCIR-12 Wikipedia Formula Browsing Task, our model can outperform previous formula search engines in Bpref prediction metrics. In addition, compared with traditional tree-based models, NTFEM not only improves the retrieval effect, but also greatly reduces the training time and improves training efficiency.
{"title":"An N-ary Tree-based Model for Similarity Evaluation on Mathematical Formulae","authors":"Yifan Dai, Liangyu Chen, Zihan Zhang","doi":"10.1109/SMC42975.2020.9283495","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283495","url":null,"abstract":"Accurate and efficient measurements for evaluating the similarity between mathematical formulae play an important role in mathematical information retrieval. Most previous studies have focused on representing formulae in different types to catch their features and combining the traditional structure matching algorithms. This paper presents a new unsupervised model called N-ary Tree-based Formula Embedding Model (NTFEM) for the task of mathematical similarity evaluation. Using an n-ary tree structure to represent the formula, we convert the formula into a linear sequence that can be viewed as the input sentence and then embed the formula by using a word embedding model. Based on the characteristics of mathematical formulae, a weighting function is also used to get the final weighted average embedding vector. Through some experiments on NTCIR-12 Wikipedia Formula Browsing Task, our model can outperform previous formula search engines in Bpref prediction metrics. In addition, compared with traditional tree-based models, NTFEM not only improves the retrieval effect, but also greatly reduces the training time and improves training efficiency.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"103 1","pages":"2578-2584"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91423652","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9282824
Wenqiang Peng, Qingjian Ni
Weather prediction is one of the hot topics in artificial intelligence. In this paper, three new temperature prediction models based on historical data are proposed for two important meteorological indexes, the maximum temperature and the minimum temperature. The first model is to construct SVM model to predict the residual error of LSTM model, then add the prediction results of the two models to get the final prediction result. The second model is to use empirical mode decomposition (EMD) to decompose the original data, then use the combination forecasting model to predict the subsequences, and finally summarize the prediction results. The third model is to combine the advantages of the first and second models. First, EMD is used to decompose the original sequence. Then, the first model is used to predict each subsequence. Finally, the predicted values of all subsequences are superimposed to obtain the final predicted value. Based on the temperature data of Washington and Los Angeles, the three models are tested and analyzed in this paper. The experimental results show that the third model proposed in this paper, which is based on EMD and residual prediction SVM-LSTM model, has better prediction accuracy than other models.
{"title":"A Hybrid SVM-LSTM Temperature Prediction Model Based on Empirical Mode Decomposition and Residual Prediction","authors":"Wenqiang Peng, Qingjian Ni","doi":"10.1109/SMC42975.2020.9282824","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282824","url":null,"abstract":"Weather prediction is one of the hot topics in artificial intelligence. In this paper, three new temperature prediction models based on historical data are proposed for two important meteorological indexes, the maximum temperature and the minimum temperature. The first model is to construct SVM model to predict the residual error of LSTM model, then add the prediction results of the two models to get the final prediction result. The second model is to use empirical mode decomposition (EMD) to decompose the original data, then use the combination forecasting model to predict the subsequences, and finally summarize the prediction results. The third model is to combine the advantages of the first and second models. First, EMD is used to decompose the original sequence. Then, the first model is used to predict each subsequence. Finally, the predicted values of all subsequences are superimposed to obtain the final predicted value. Based on the temperature data of Washington and Los Angeles, the three models are tested and analyzed in this paper. The experimental results show that the third model proposed in this paper, which is based on EMD and residual prediction SVM-LSTM model, has better prediction accuracy than other models.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"55 1","pages":"1616-1621"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83735616","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 : 2020-10-11DOI: 10.1109/SMC42975.2020.9282930
Yasuhiro Miura, Yuki Sawamura, Yuki Shinomiya, Shinichi Yoshida
Vegetable mass estimation from monocular RGB camera images is proposed. Vegetables are fragmented and placed on a conveyor belt of food processing machine and the monocular camera placed over the belt take pictures of vegetables on the belt. The proposed system does not employ any scale, load cell, and other mass scaling equipment. We apply pre-trained convolutional neural networks to estimate the mass of vegetables. Transfer learning including various levels of fine-tuning is also applied. For pre-trained network, we use Xception, VGG16, ResNet50, and Inception_v3, which are pre-trained using ImageNet. The result shows that the best estimation accuracy is achieved by VGG16, whose MAPE (mean average percentage error) is 11.1%. Additionally, we fine-tune VGG16 and the accuracy reduces to 7.9% for MAPE. From this result, the performance of CNN model can improve by fine-tuning. The proposed system can be applied to low-cost, high-speed, and efficient measurement of foods replaced to load cells.
{"title":"Vegetable Mass Estimation based on Monocular Camera using Convolutional Neural Network","authors":"Yasuhiro Miura, Yuki Sawamura, Yuki Shinomiya, Shinichi Yoshida","doi":"10.1109/SMC42975.2020.9282930","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282930","url":null,"abstract":"Vegetable mass estimation from monocular RGB camera images is proposed. Vegetables are fragmented and placed on a conveyor belt of food processing machine and the monocular camera placed over the belt take pictures of vegetables on the belt. The proposed system does not employ any scale, load cell, and other mass scaling equipment. We apply pre-trained convolutional neural networks to estimate the mass of vegetables. Transfer learning including various levels of fine-tuning is also applied. For pre-trained network, we use Xception, VGG16, ResNet50, and Inception_v3, which are pre-trained using ImageNet. The result shows that the best estimation accuracy is achieved by VGG16, whose MAPE (mean average percentage error) is 11.1%. Additionally, we fine-tune VGG16 and the accuracy reduces to 7.9% for MAPE. From this result, the performance of CNN model can improve by fine-tuning. The proposed system can be applied to low-cost, high-speed, and efficient measurement of foods replaced to load cells.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"61 1","pages":"2106-2112"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79603424","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}
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