2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)最新文献_第3页

Particle swarm optimization for coconut detection in a coconut tree plucking robot 基于粒子群算法的椰树采摘机器人椰子检测

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228584

Alfin Junaedy, I. A. Sulistijono, Nofria Hanafi

{"title":"Particle swarm optimization for coconut detection in a coconut tree plucking robot","authors":"Alfin Junaedy, I. A. Sulistijono, Nofria Hanafi","doi":"10.1109/KCIC.2017.8228584","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228584","url":null,"abstract":"High risk of climbing coconut tree manually become the main reason to build coconut tree plucking robot, not only the abnormality of bone but also the risk of falling from the coconut tree. The coconut tree plucking robot is made with the hope for helping people to pluck coconuts at the coconut tree easily and safely. Coconut tree with its condition make the coconuts difficult to be detected using image processing. Previous methods which are only work in indoor, only detect a coconut and only work on nearly uniform background are not suitable and easy to be disturbed with the interferences from the real condition in a coconut tree. An image processing with particle swarm optimization (PSO) method is introduced in this paper. It will find the best position of the coconuts at the tree and pluck it by giving a command to the arm to move toward the coconuts and cut its base by turning the grinder on the top of arm. Experiment results show that successful rate of the method to detect coconuts at the tree with cluttered background is 80% and then pluck them using the robot arm.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"1986 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125469881","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}

引用次数: 4

Improved ejection fraction measurement on cardiac image using optical flow 改进的射血分数心脏图像光流测量方法

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228602

Arvina Aulia Pratiwi, R. Sigit, D. Basuki, Y. Oktaviono

引用次数: 8

Utilization image of monocular camera to build navigation system and path mapping using SURF approach 利用单目相机图像构建导航系统，利用SURF方法进行路径映射

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228577

Berlian Rahmy Lidiawaty, Nana Ramadijanti, E. S. Ningrum

{"title":"Utilization image of monocular camera to build navigation system and path mapping using SURF approach","authors":"Berlian Rahmy Lidiawaty, Nana Ramadijanti, E. S. Ningrum","doi":"10.1109/KCIC.2017.8228577","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228577","url":null,"abstract":"Navigation system of some camera devices such as robot could be done by measure the rotation of device's wheels and device's velocity. But in several cases, like device for rescue and surveillance, sometimes the device need to deal with an uneven surface that makes device's wheels slipped, impacting in navigation measurement. Fortunately, almost all of devices nowadays equipped with at least one camera to monitor the environment. So, this research purpose to utilization image of the camera vision that's been captured to build a navigation system. We will show that a simple monocular camera could build an alternative navigation system for several uses, while the system also navigates and tracks camera's paths to draw the path mapping. Firstly, we tried to detect the camera motion and its direction by using optical flow model. To improve this model, we use feature transform, which is SURF. Secondly, after we know the direction from feature transform, we got the formula to know the real direction and use it for drawing the path that camera took for tracking. We tested our application directly in our video stream camera and compare the system coordinate with the real world coordinate in centimeters unit.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123925672","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}

引用次数: 2

Segmentation of head CT-scan to calculate percentage of brain hemorrhage volume 分割头部ct扫描计算脑出血体积百分比

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228603

Rizal Romadhoni Hidayatullah, R. Sigit, Sigit Wasista

{"title":"Segmentation of head CT-scan to calculate percentage of brain hemorrhage volume","authors":"Rizal Romadhoni Hidayatullah, R. Sigit, Sigit Wasista","doi":"10.1109/KCIC.2017.8228603","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228603","url":null,"abstract":"Brain hemorrhage is a serious category of head injury that can have a fatal impact on brain function and performance. But sometimes the identification of cerebral hemorrhage can not be known immediately. So far, the identification of cerebral hemorrhage is done through CT Scan image observation that requires special skills. Therefore we need a certain method that can segment the CT Scan image quickly and automated. The goal is to obtain the image segmentation of brain bleeding more quickly and accurately. So patients with cerebral hemorrhage can immediately obtain medical treatment in accordance with the needs. The preprocessing process of CT Scan image starts from the preprocessing phase of the CT Scan image using color filtering, erosion and dilation methods. This stage is done to clarify the cerebral hemorrhage and eliminate the noise contained in the image. Then performed watershed and cropping segmentation to separate the skull bones of the skull with brain tissue. The next step is to improve the image quality using median filtering. Then the image is again segmented using the threshold method to separate the image of cerebral hemorrhage as the observed object. Last performed the calculation of area and volume percentage of bleeding in the brain. From the system test obtained the calculation of brain area has an average error of 1.13%. As for the test calculation of the area of bleeding has an average error of 11.17%.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452764","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}

引用次数: 7

E-voting security system for the election of EEPIS BEM president EEPIS BEM总裁选举电子投票保障系统

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228578

Zuyina Ayuning Saputri, Amang Sudarsono, Mike Yuliana

{"title":"E-voting security system for the election of EEPIS BEM president","authors":"Zuyina Ayuning Saputri, Amang Sudarsono, Mike Yuliana","doi":"10.1109/KCIC.2017.8228578","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228578","url":null,"abstract":"The Development of technological system have grown up rapidly including how to vote. In general, people are accustomed to vote by using ballot paper. In Indonesia, this way is considered ineffective both in terms of cost, time, and governance. Due to this, the creation of an electronic voting system or known as e-voting based on android for the voting system implemented can be performed more easily and effectively. It takes a short time, the cost is cheaper, and it can minimize errors. However, currently existing e-voting still has a weakness in terms of security that can allow a person manipulate the data of the voting result. Therefore, e-voting requires a security method that can guarantee the authenticity of the voting data. In this case the 1024-bit Shamir's algorithm is used for anonymity voter authentication. With the feature of anonymity of voters (secrecy of voter identity) so that achieved is one of the most important electoral principle is “Secret”. Moreover, it keeps the authenticity of data voting results used SHA-1 algorithm. From the test results, the average time for generating key by Shamir's algorithm is 552.8 milliseconds. The fastest average time for computation process comprises signing, transmission and validation time is 3747.1 milliseconds.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126125236","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}

引用次数: 12

A method for reducing the amounts of training samples for developing AI systems 一种用于减少开发AI系统的训练样本数量的方法

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228448

Mami Nagoya, Kei Shiohara, Xing Chen

引用次数: 0

Road edge detection on 3D point cloud data using Encoder-Decoder Convolutional Network 基于编解码器卷积网络的三维点云数据道路边缘检测

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228570

R. F. Rachmadi, K. Uchimura, G. Koutaki, K. Ogata

{"title":"Road edge detection on 3D point cloud data using Encoder-Decoder Convolutional Network","authors":"R. F. Rachmadi, K. Uchimura, G. Koutaki, K. Ogata","doi":"10.1109/KCIC.2017.8228570","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228570","url":null,"abstract":"The demand of High Definition Maps (HD-Maps) has been increasing, especially for autonomous vehicle application. Usually, HD-Map is created by scanning the road using LiDAR sensor and reconstructing the road on 3D world to capture all aspects of road properties. One of the important properties of a road is its edge or boundary. In this paper, we propose end-to-end 3D Encoder-Decoder Convolutional Network (3D-EDCN) for road edge detection on 3D point cloud data produced by LiDAR sensor. Our 3D-EDCN classifier consists of nine convolutional layers and three deconvolutional layers. For simplification, we use 3D voxel format as input and output of the classifier. Our proposed method was tested using our own 3D point cloud dataset which taken from LiDAR equipment and consisting of 103 3D point cloud data with their respective road edge ground truth. In the training process, we use combinations of Cross-Entropy loss function and Euclidean loss function to help our model converged. As a preliminary result, our proposed 3D-EDCN classifier achieves Mean Square Error (MSE) of 2.738×10−5, precision of 0.37262, and recall of 0.14432.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"35 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016931","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}

引用次数: 10

Whirlwind prediction using cloud movement patterns on satellite image 利用卫星图像上的云运动模式预测旋风

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228595

Rhoma Cahyanti, Rendra Budi Hutama, Rafi Haidar Ramdlon, Windasari Dwiastuti, Fadilah Fahrul Hardiansyah, A. Basuki

{"title":"Whirlwind prediction using cloud movement patterns on satellite image","authors":"Rhoma Cahyanti, Rendra Budi Hutama, Rafi Haidar Ramdlon, Windasari Dwiastuti, Fadilah Fahrul Hardiansyah, A. Basuki","doi":"10.1109/KCIC.2017.8228595","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228595","url":null,"abstract":"Whirlwind is a local-scale meteorological phenomenon that occurs in a short time, destructive, and can cause loss of life and material. Until now, when and where the whirlwind will occur can not be predicted precisely. However, the signs are still recognizable from some of the symptoms before the phenomenon occurs, such as high temperatures and the formation of many Cumulus clouds which then suddenly transform into Cumulonimbus clouds. Because this incident caused a lot of damage and casualties, the whirlwind needs to be predicted, so that later people can be more vigilant and the impact can be minimized. This research aims to build a system for taking cloud movement patterns from observing cloud clusters on the satellite image. The clustering method is used to classify clouds, and then find out the pattern of movement in each cluster. This pattern of movement is a model to predict the occurrence of the whirlwind. The results obtained from the experiments in several whirlwind incidents in Indonesian territory indicate that at 24 hours before the event, there are at least Cumulus, Middle Cloud and/or Stratocumulus clouds that have a curving pattern; approaching and then away from the location where the whirlwind appears. Furthermore, the pattern of cloud movement will be collected to build a data test. The results obtained from the K-NN method show the accuracy of the data test collected from a number of the whirlwind phenomenon in 2016 is 88%. Meanwhile, when the data test tested with SVM method, the percentage of accuracy is 84%.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131146111","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}

引用次数: 5

Forensic identification system using dental panoramic radiograph 利用牙科全景x线摄影技术的法医鉴定系统

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228600

Nabilah Ayu Permata, Setiawardhana, R. Sigit

{"title":"Forensic identification system using dental panoramic radiograph","authors":"Nabilah Ayu Permata, Setiawardhana, R. Sigit","doi":"10.1109/KCIC.2017.8228600","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228600","url":null,"abstract":"Forensic odontology is one method of determining the identity of the individuals who use it as a base dental identification. Teeth can provide information about the individual's identity because of its distinctive. Currently, the process of forensic identification through dental radiography is performed manually so it took a long time to match the teeth with human identity. Therefore, we need a system that can identify human identity quickly and accurately through dental radiography. In this experiment develop a system to identify human identity through dental radiography image quickly and accurately. The method of active shape model used for segmentation of mandibular teeth, this method can do shape search, to get the most appropriate shape. So that will get contours in accordance with the desired contour. This can help provide great results for the next stage. Then the extraction method used is Hu moment invariant, from this stage we will get 7 values that are free of rotation and scaling, so later if there is radiography that occurs the shift will still be detected. This experiment used 20 training data for segmentation, and 75 data dental radiograph images that has different shift for matching stage. The results of this research show success with an average of 82.67%. The proposed research method for this forensic identification system can be an appropriate system for the process of identifying a person based on dental radiography images.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131160102","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}

引用次数: 1

Big data analysis architecture for multi IDS sensors using memory based processor 基于内存处理器的多IDS传感器大数据分析架构

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228456

Ferry Astika Saputra, Muhammad Salman, K. Ramli, A. Abdillah, I. Syarif

{"title":"Big data analysis architecture for multi IDS sensors using memory based processor","authors":"Ferry Astika Saputra, Muhammad Salman, K. Ramli, A. Abdillah, I. Syarif","doi":"10.1109/KCIC.2017.8228456","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228456","url":null,"abstract":"The massive internet usage is followed by the rise of cyber-related crime such as information stealing, denial-of-service (DoS) attack, trojan and malware. To cope with the threats, one of most popular choice is using Intrusion Detection System (IDS). The logs produced by IDS in a day is huge and the limitation of computing power is the main problem to process that logs files. In this paper, we propose a big data analysis architecture of multi IDS sensors using in-memory data processing. Deployed IDS sensors are taking an extra role as computation slave to build scalable data analysis platform for network security analysis. So, adding more sensors means expanding computational resources. Adding to three sensors are helping data computation of clustering algorithm faster up to 27% comparing to the computation by using only one sensor. This research also introduces the use of memory-based processor, this system provides 7,9 times faster data processing than conservative MapReduce operation. And moreover, we also have performed botnets classification over Spark RDD that give high accuracy result to 99%.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475053","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}

引用次数: 2