2021 International Conference on Instrumentation, Control, and Automation (ICA)最新文献

{"title":"Classification of Emotion Based on Electroencephalogram Using Convolutional Neural Networks and Recurrent Neural Networks","authors":"S. Sundari, E. C. Djamal, Arlisa Wulandari","doi":"10.1109/ICA52848.2021.9625702","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625702","url":null,"abstract":"Emotional recognition is widely used in various fields, one of which monitors emotions in understanding the processes that occur in each individual. Electroencephalogram (EEG) can capture emotional information objectively. Nevertheless, it needs appropriate processing. The multichannel of EEG gives much information that yields redundancies. Thus, it can view the information from the multichannel spatially and the sequence between signals as temporal in processing. Several methods are often used, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The combination of CNN and RNN utilizes a large amount of information from multi-channel and reserves the characteristics of RNN for processing sequence data such as EEG signals. So that it can maintain the signal sequence information of each channel, this paper proposed the CNN-RNN to identify three positive, negative, and neutral classes of emotions. The EEG signal was filtered at a frequency of 4-45 Hz, according to the signal characteristics of the three emotion classes. It used Wavelet to get Theta, Alpha, Beta, and Gamma waves. The results showed that the use of CNN as multi-channel handling could increase the accuracy, from 91.98%, compared to 79.52% with only RNN. On the other side, CNN-RNN can provide a shorter computation time. However, the choice of emotional duration is significant. Experiments suggest that a second represents more emotional change than five seconds.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509642","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}

{"title":"ICA 2021 Conference Organization","authors":"","doi":"10.1109/ica52848.2021.9624481","DOIUrl":"https://doi.org/10.1109/ica52848.2021.9624481","url":null,"abstract":"","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"29 Pt 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602058","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}

{"title":"Discriminant Feature Extraction of Motor Current Signal Analysis and Vibration For Centrifugal Pump Fault Detection","authors":"Asma’ul Husna, K. Indriawati, B. L. Widjiantoro","doi":"10.1109/ICA52848.2021.9625679","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625679","url":null,"abstract":"The monitoring condition of the centrifugal pump is closely related to fault detection and diagnosis. It usually uses the vibration signals. However, under certain conditions it is not possible to install the accelerometer on the machine due to certain conditions and environments. Current signals can be used to replace vibration signals. This method is called motor current signature analysis (MCSA). The raw signal of the current and current spectrum in frequency domain can be used for fault detection. The statistical features of the current raw signal contain information on the signal characteristics. However, these raw features are not sensitive enough to weak fault symptoms or are not suitable for severe faults, thus can affect fault detection and classification accuracy. To overcome this problem, discriminant feature extraction is carried out for fault detection in centrifugal pumps (CP). Discriminant features are divided into four phases. In the first phase, a healthy pump signal is selected. In the second phase, the healthy condition signal is cross-correlated with the centrifugal pump current signal in several fault classes and the result of the extraction from the cross correlation is a new feature set. In the third phase, the raw statistical features in the time, frequency and time-frequency domains are extracted from both healthy current signals and CP current signals of different classes. In the last phase, wavelet packet transform (WPT) energy is extracted from the current signals. The result of these features will be combined into a discriminant feature pool. The pool discriminant feature will be used as input in making a classifier for the centrifugal pump fault detection system. This study also used motor bearing speed data for comparison. The main topic of this paper is to design a fault detection system for centrifugal pumps using current signals. Based on the performance test using precision, error rate, and recall. The motor bearing speed vibration signal has better performance than the CP fault detection classification with the current signal. However, there is only a slight difference between the two. From this research, the current signal and motor bearing speed vibration signal can detect fault to the centrifugal pump well.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124645767","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}

{"title":"Localization of Autonomous Vehicles in Various Environment Conditions using ORB-SLAM2","authors":"Farah Elma Annisa, Salma Putri Salvira, Y. Y. Nazaruddin, A. Widyotriatmo, Undiana Bambang","doi":"10.1109/ICA52848.2021.9625688","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625688","url":null,"abstract":"Perception is one of the key components of autonomous vehicle. Autonomous vehicle percepts the environment before it decides what to do next. Inaccurate perception can cause wrong motion planning and accident, e.g. inaccurate localization for position estimation using GNSS (Global Navigation Satellite System) if it is implemented in closed area such as under the tree or inside a building. In this paper, an alternative method will be introduced to get an accurate localization of autonomous vehicle in various environment condition by using Visual Simultaneous Localization and Mapping (V-SLAM) approach. For this purpose, ORB (Oriented FAST and Rotated BRIEF) will be applied as mapping method combined with RGBD depth camera as a sensor. ORB-SLAM2 method is used due to its computing speed and accuracy which are significant factors for implementing perception mechanism in an autonomous vehicle. Experimental investigation for controlling a golf cart integrated with ORB-SLAM2 method for perception mechanism has been conducted to observe the potential and performance of the proposed method.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127045979","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}

{"title":"A Robot Speed Boundary Control Strategy","authors":"Jimmy H. Smith","doi":"10.1109/ICA52848.2021.9625694","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625694","url":null,"abstract":"For the ground mobile robot, the moving speed is a crucial concern when it comes to the applications like, vison-based object detection, map building. In this case, a controller that can strictly regulate the robot speed with the undesired trajectory is need. A mobile robot control strategy is proposed to achieve a satisfactory trajectory tracking task, and at the same time, achieve the speed regulation or limitation task. Compared to traditional methods, the proposed method no longer needed extra output calculation or saturation to the control output, which will avoid the unexpected oscillations and errors. Moreover, the proposed controller has the advantage of that, even if the provided reference signal is larger, or say, violates the desired boundary, the proposed controller can still be able to drive the robot speed within the desired boundary. In addition, the boundary regulation can be ensured, when the given reference signals are not ideal. The stated reference signal is not considered suitable in this work. Moreover, the stabilization and tracking speed regulation problem has been conducted, and the simulation results are also provided with the demonstration of its superiority over the traditional mobile robot control methods.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124243499","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}

{"title":"Prototypes for Measuring Peroxide Concentration in Cooking Oil Using a Green Laser","authors":"V. Nadhira, E. Juliastuti, Kembang Swasti Reninggalih, Jesika Andilia Setya Wardani","doi":"10.1109/ICA52848.2021.9625675","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625675","url":null,"abstract":"Cooking oil as a food processing medium is commonly used by Indonesian people. However, most traders repeatedly use cooking oil to reduce production costs without paying attention to the quality decrease of the used oil. One of the parameters of oil quality degradation is the increase in peroxide number. Peroxide is a compound that triggers free radicals that cause cancer in the body human body. Therefore, the government supervises the use of cooking oil by testing it in the laboratory. Such testing requires a large amount of time, money, and energy. Therefore, in this study, a prototype of a portable peroxide level measuring instrument was made. This prototype utilizes a green laser diode as a light source. The sample used is mixed cooking oil with reagents. The mixing causes the sample to change color to purplish. The greater the rate of peroxide, the more purple the sample will be. Under these conditions, the sample absorbs visible light at 510-590nm range. Arduino will proceed the light transmission data into peroxide concentrations. Based on the test results, this prototype can be used to measure the concentration of peroxide properly in the measuring range 5.57–14.86 mEq O2/kg. The accuracy of this measuring instrument is 91.55%, with a precision value of 89.66%.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117125842","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}

{"title":"Cross Validation Configuration on k-NN for Finger Movements using EMG signals","authors":"K. Anam, Harun Ismail, F. S. Hanggara, Cries Avian, S. Worsito","doi":"10.1109/ICA52848.2021.9625699","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625699","url":null,"abstract":"It is already widely known that hand presence is very vital to perform daily activities successfully. However, it can be a different condition for people living with disabilities. They experience difficulty getting their activities done. Any tools that can replace hand movements are essential, such as hand prosthetics and hand exoskeleton. Nonetheless, the increasing accuracy score of hand motion classification using electromyography (EMG) signals must be further explored. This paper proposes a way to improve the accuracy of twelve finger movements classification task using k-Nearest Neighbor (k-NN) with two kinds of cross-validations, KFold and Stratified KFold evaluation. Before the EMG signal is fed to k-NN, this signal derived from four able-bodied subjects is extracted using Mean Absolute Value (MAV). Using k values of 1, 3, 5, and 7 on k-NN and shuffle and non-shuffle data on cross-validation, this study shows a comparative result of the above combination and directs future work. The shuffle data's accuracy rate outperforms the non-shuffle data with an accuracy of 89.97% compared to 65.39% on KFold and 90.24% compared to 82.08% on Stratified KFold. From this outcome, the shuffle data process significantly affects the accuracy level on hand movement classification.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129856279","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}

{"title":"ICA 2021 TOC","authors":"","doi":"10.1109/ica52848.2021.9625667","DOIUrl":"https://doi.org/10.1109/ica52848.2021.9625667","url":null,"abstract":"","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125652398","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}

{"title":"Object Matching in Autonomous Vehicle Perception System Using ORB Feature Matching with SVM Classifier","authors":"Rio Ariesta Sasmono, Muhammad Iqbal Anggoro Agung, Y. Y. Nazaruddin, Joshua Abel Oktavianus, Gilbert Tjahjono","doi":"10.1109/ICA52848.2021.9625698","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625698","url":null,"abstract":"Autonomous vehicles require a well-developed perception system to ensure the accuracy of action decision making. This work is focused on the object tracking using object matching as part of this perception system. The application of ORB object matching algorithm is proposed to extract eight different features from numerous image pairs, which is then processed by a Support Vector Machine (SVM) technique as machine learning model to increase matching accuracy. The object matching algorithm is optimized using a simple genetic algorithm to minimize a failed feature extraction on low resolution images. The SVM model was tested using four different kernels with different parameters and used a grid-based optimizing method. It is found that the SVM linear kernel performed the best, and that the Generalized Intersection-Over-Union feature was very dominant in determining matching image pairs. The SVM model used in this investigation resulted in an accuracy of 96%, although numerous bad anomalies were also found.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751718","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}

{"title":"Container Detection System Using CNN Based Object Detectors","authors":"Steven Bandong, Y. Y. Nazaruddin, E. Joelianto","doi":"10.1109/ICA52848.2021.9625663","DOIUrl":"https://doi.org/10.1109/ICA52848.2021.9625663","url":null,"abstract":"The increase in free trade will also amplify the exchange of goods between countries and islands, especially in the seaports. The manual operation of the gantry-crane at the seaports has a risk due to human negligence. For that reason, automation is strongly needed in the loading and unloading of the container. Camera-based object detection is implemented to achieve this goal. In the recent years, CNN gives better accuracy in term of image classification, recognition and detection compared to the traditional method that use handcrafted features. In this paper, several advanced detection methods using CNN-based object detection, namely MobileNet, ResNet, and Faster RCNN are compared to detect and track the movement of containers. The results of the experiment show that the SSD ResNet and Faster RCNN methods are superior in terms of detection accuracy based on the Precision, Recall, Average Precision, and IoU values. MobileNet v3 excels in speed detection compared to the other methods.","PeriodicalId":287945,"journal":{"name":"2021 International Conference on Instrumentation, Control, and Automation (ICA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133633857","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}