Pub Date : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524322
N. Fanani, U. D. Rosiani, S. Sumpeno, M. Purnomo
Decision-making process typically involves multiple attributes. It is using a part or whole attributes to find the best decision from the alternatives. Some methods such as rough set are used to solve this problem but it has worse time complexity with respect to the numerous attributes. Hence, Multi Rough Set is proposed to improve the performance of rough set. In this study, this method used to classify the anger of Javanese woman's which require numerous attributes but has limited number of object. We divided the information table into several groups which has similarity attribute and it is computed simultaneously. The decision of each group as result of rough set and then used fuzzy rule set to obtain the final result. Using leave one out cross validation obtained 79% more accurate than using single rough set for all attribute.
{"title":"Multi attribute decision making model using multi rough set: Case study classification of anger intensity of Javanese woman","authors":"N. Fanani, U. D. Rosiani, S. Sumpeno, M. Purnomo","doi":"10.1109/CIVEMSA.2016.7524322","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524322","url":null,"abstract":"Decision-making process typically involves multiple attributes. It is using a part or whole attributes to find the best decision from the alternatives. Some methods such as rough set are used to solve this problem but it has worse time complexity with respect to the numerous attributes. Hence, Multi Rough Set is proposed to improve the performance of rough set. In this study, this method used to classify the anger of Javanese woman's which require numerous attributes but has limited number of object. We divided the information table into several groups which has similarity attribute and it is computed simultaneously. The decision of each group as result of rough set and then used fuzzy rule set to obtain the final result. Using leave one out cross validation obtained 79% more accurate than using single rough set for all attribute.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131240231","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 : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524315
Yazhou Wang, Yong Hu
Somatosensory evoked potentials (SEP) have been found to contain a series of time-frequency components that conveys information about the location of neurological deficits within the spinal cord. This study aims to develop a classification system for identifying the location of neurological deficit in cervical spinal cord based on the time-frequency patterns of SEPs. Waveforms of SEPs after compressive injuries at various locations (C4, C5, and C6) of rats' spinal cord were decomposed into a series of time-frequency components (TFCs) by a high resolution time-frequency analysis method, matching pursuit (MP). A classification system was build according to the distributional distinction of these TFCs among different levels using support vector machine (SVM). This distinction manifests itself in different categories of SEP TFCs. High-energy TFCs of normal state SEP have significantly higher power and frequency compared with those of injury state SEP. The level of C5 is characterized by a unique distribution pattern of middle-energy TFCs. And the difference between C4 and C6 level is evidenced by the distribution pattern of low-energy TFCs. The proposed classification system was proved to be able to distinguish the four functional status (normal, injury at C4, C5, and C6) with an accuracy of 80.17%.
{"title":"Identifying the location of spinal cord injury by support vector machines using time-frequency features of somatosensory evoked potentials","authors":"Yazhou Wang, Yong Hu","doi":"10.1109/CIVEMSA.2016.7524315","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524315","url":null,"abstract":"Somatosensory evoked potentials (SEP) have been found to contain a series of time-frequency components that conveys information about the location of neurological deficits within the spinal cord. This study aims to develop a classification system for identifying the location of neurological deficit in cervical spinal cord based on the time-frequency patterns of SEPs. Waveforms of SEPs after compressive injuries at various locations (C4, C5, and C6) of rats' spinal cord were decomposed into a series of time-frequency components (TFCs) by a high resolution time-frequency analysis method, matching pursuit (MP). A classification system was build according to the distributional distinction of these TFCs among different levels using support vector machine (SVM). This distinction manifests itself in different categories of SEP TFCs. High-energy TFCs of normal state SEP have significantly higher power and frequency compared with those of injury state SEP. The level of C5 is characterized by a unique distribution pattern of middle-energy TFCs. And the difference between C4 and C6 level is evidenced by the distribution pattern of low-energy TFCs. The proposed classification system was proved to be able to distinguish the four functional status (normal, injury at C4, C5, and C6) with an accuracy of 80.17%.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128712703","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 : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524314
Thomas Calloway, D. Megherbi
Systems that use low cost depth sensors, to perform 3D reconstructions of environments while simultaneously tracking sensor pose, have received significant attention in recent years. While the majority of publications in the literature on the subject focus on the successes of various 3D scene reconstruction algorithms used, few attempt to quantify the practical limitations of the RGB-D sensors themselves. Furthermore, many publications report successful results while ignoring the many situations in which the systems will be entirely non-functional. In our prior work, using an optical-inertial motion tracker, we evaluated 3 Degree-Of-Freedom (3 DOF) sensor orientation estimation errors existing in a Simultaneous Localization and Mapping (SLAM) implementation based on the popular Microsoft Kinect. In this paper we present and extend our analysis of 3 DOF sensor orientation estimation error, using an optical-inertial motion tracker, to include the full 6 DOF sensor pose (positioning and orientation). We then fully integrate the motion tracker into the original depth sensor-based algorithm, demonstrating improved reliability and accuracy of scene reconstruction.
{"title":"Using 6 DOF vision-inertial tracking to evaluate and improve low cost depth sensor based SLAM","authors":"Thomas Calloway, D. Megherbi","doi":"10.1109/CIVEMSA.2016.7524314","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524314","url":null,"abstract":"Systems that use low cost depth sensors, to perform 3D reconstructions of environments while simultaneously tracking sensor pose, have received significant attention in recent years. While the majority of publications in the literature on the subject focus on the successes of various 3D scene reconstruction algorithms used, few attempt to quantify the practical limitations of the RGB-D sensors themselves. Furthermore, many publications report successful results while ignoring the many situations in which the systems will be entirely non-functional. In our prior work, using an optical-inertial motion tracker, we evaluated 3 Degree-Of-Freedom (3 DOF) sensor orientation estimation errors existing in a Simultaneous Localization and Mapping (SLAM) implementation based on the popular Microsoft Kinect. In this paper we present and extend our analysis of 3 DOF sensor orientation estimation error, using an optical-inertial motion tracker, to include the full 6 DOF sensor pose (positioning and orientation). We then fully integrate the motion tracker into the original depth sensor-based algorithm, demonstrating improved reliability and accuracy of scene reconstruction.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"445 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126054327","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}
Wireless Sensor Network has been one of the most diversified and widely used area and has a vast range of has applications in almost every field. Wireless Sensor Network is a domain with a large number of motes that collects data from the surrounding and after processing, it transfers the data to the sink node through intermediate sensor node after which it is finally transmitted to the base station. With wide range of applications, Wireless Sensor Network comes up with one of the major issues i.e. Energy Consumption. Due to the dense network topology of WSN, the communication range is short which incurs a redundancy in the sensed data. To reduce this redundancy, Data Aggregation and Data Fusion are most effective as it helps in saving both data and energy. This paper proposes a Secure and energy efficient architecture that takes account of the constraints of sensor networks. A hierarchical network topology is formed that enables end-to-end communication between sensor nodes and the architecture also supports the detection and isolation of malicious nodes. Moreover, security issues of a wireless sensor network are also a major concern and so, we aim to design a secured architecture for reliable and safe data communication. To make the system energy efficient, Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm has been used. Algorithm also helps in the secure transmission of data and also conserves energy. By doing simulations in NS2 we found that it saves energy and hence enhances the lifetime of a mote.
{"title":"Secured and energy efficient architecture for sensor networks","authors":"Jetendra Joshi, Amrit Bagga, Abhinandan Bhargava, Abhinav Goel, Divya Kurian, Urijit Kurulkar","doi":"10.1109/CIVEMSA.2016.7524319","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524319","url":null,"abstract":"Wireless Sensor Network has been one of the most diversified and widely used area and has a vast range of has applications in almost every field. Wireless Sensor Network is a domain with a large number of motes that collects data from the surrounding and after processing, it transfers the data to the sink node through intermediate sensor node after which it is finally transmitted to the base station. With wide range of applications, Wireless Sensor Network comes up with one of the major issues i.e. Energy Consumption. Due to the dense network topology of WSN, the communication range is short which incurs a redundancy in the sensed data. To reduce this redundancy, Data Aggregation and Data Fusion are most effective as it helps in saving both data and energy. This paper proposes a Secure and energy efficient architecture that takes account of the constraints of sensor networks. A hierarchical network topology is formed that enables end-to-end communication between sensor nodes and the architecture also supports the detection and isolation of malicious nodes. Moreover, security issues of a wireless sensor network are also a major concern and so, we aim to design a secured architecture for reliable and safe data communication. To make the system energy efficient, Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm has been used. Algorithm also helps in the secure transmission of data and also conserves energy. By doing simulations in NS2 we found that it saves energy and hence enhances the lifetime of a mote.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334956","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 : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524251
R. D. Labati, A. Genovese, E. M. Ballester, V. Piuri, F. Scotti, Gianluca Sforza
The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles.
{"title":"Analyzing images in frequency domain to estimate the quality of wood particles in OSB production","authors":"R. D. Labati, A. Genovese, E. M. Ballester, V. Piuri, F. Scotti, Gianluca Sforza","doi":"10.1109/CIVEMSA.2016.7524251","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524251","url":null,"abstract":"The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123815023","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 : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524248
David Hamblin, Dali Wang, Gao Chen
This paper presents an algorithm for classifying measurement variables within airborne measurement data files collected by NASA. The proposed solution utilizes a combination of decision tree and Naive Bayes classifiers. In order to mitigate the independence assumption of Naive Bayes, we apply a weight vector to the feature set based on each feature's role in the classification process. The Analytic Hierarchy Process is selected to calculate the weight vector, after an investigation of various weight calculation techniques. The assessment of the algorithm with recent NASA data shows that the algorithm delivers robust results, and exceeds the performance expectation in the presence of inconsistencies and inaccuracies among measurement data.
{"title":"Measurement classification using hybrid weighted Naive Bayes","authors":"David Hamblin, Dali Wang, Gao Chen","doi":"10.1109/CIVEMSA.2016.7524248","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524248","url":null,"abstract":"This paper presents an algorithm for classifying measurement variables within airborne measurement data files collected by NASA. The proposed solution utilizes a combination of decision tree and Naive Bayes classifiers. In order to mitigate the independence assumption of Naive Bayes, we apply a weight vector to the feature set based on each feature's role in the classification process. The Analytic Hierarchy Process is selected to calculate the weight vector, after an investigation of various weight calculation techniques. The assessment of the algorithm with recent NASA data shows that the algorithm delivers robust results, and exceeds the performance expectation in the presence of inconsistencies and inaccuracies among measurement data.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"7 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816540","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 : 2016-06-27DOI: 10.1109/CIVEMSA.2016.7524249
D. Megherbi, Minsuk Kim
In many large infrastructures, such as military battlefields, transportation and maritime systems spanning hundreds of miles at a time, collaborative multi-agent based monitoring is important. Agent Reinforcement Learning (RL), in general, becomes more challenging in a dynamic complex cluttered environment for autonomous path planning, where agents could be moving randomly to reach their respective goals. In our previous work we presented a hybrid master-slave and peer-to-peer system architecture, where each distributed agent knows only of a given master node, is only concerned with its assigned work load, has a limited knowledge of the environment and can, collaboratively with other agents, share learned information of the environment over a communication network. In this paper we extend our previous work and focus on (a) the study of the performance of said system and the effect of the agents' random walks on the overall system agent learning speed, when each of the distributed agents, after the random walk phase, starts its exploratory trials independently of the other agents, asynchronously, and immediately after it finishes its first exploratory trial towards a sub-goal or after its random walk phase, without waiting for the slowest agent to finish its first random walk or its first exploratory phase toward a sub-goal. (b) the effect on the agent learning speed, of using an environment-clutter-index to select agent sub-goals with the aim of reducing the agent initial random walk steps and (c) the effect of agent sharing/or not sharing environment information on the agent learning speed in such scenarios.
{"title":"A hybrid P2P and master-slave cooperative distributed multi-agent reinforcement learning technique with asynchronously triggered exploratory trials and clutter-index-based selected sub-goals","authors":"D. Megherbi, Minsuk Kim","doi":"10.1109/CIVEMSA.2016.7524249","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2016.7524249","url":null,"abstract":"In many large infrastructures, such as military battlefields, transportation and maritime systems spanning hundreds of miles at a time, collaborative multi-agent based monitoring is important. Agent Reinforcement Learning (RL), in general, becomes more challenging in a dynamic complex cluttered environment for autonomous path planning, where agents could be moving randomly to reach their respective goals. In our previous work we presented a hybrid master-slave and peer-to-peer system architecture, where each distributed agent knows only of a given master node, is only concerned with its assigned work load, has a limited knowledge of the environment and can, collaboratively with other agents, share learned information of the environment over a communication network. In this paper we extend our previous work and focus on (a) the study of the performance of said system and the effect of the agents' random walks on the overall system agent learning speed, when each of the distributed agents, after the random walk phase, starts its exploratory trials independently of the other agents, asynchronously, and immediately after it finishes its first exploratory trial towards a sub-goal or after its random walk phase, without waiting for the slowest agent to finish its first random walk or its first exploratory phase toward a sub-goal. (b) the effect on the agent learning speed, of using an environment-clutter-index to select agent sub-goals with the aim of reducing the agent initial random walk steps and (c) the effect of agent sharing/or not sharing environment information on the agent learning speed in such scenarios.","PeriodicalId":244122,"journal":{"name":"2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261971","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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