2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)最新文献_第3页

Image Retrieval of Indonesian Batik Clothing Based on Convolutional Neural Network 基于卷积神经网络的印尼蜡染服装图像检索

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972332

Mutia Fadhilla, Des Suryani, Nesi Syafitri, Hendra Gunawan

引用次数: 1

Numerical Study on Cavities Shape for the Hydrothermal Performance of Microchannel Heat Sinks with Elliptical Ribs and Secondary Channels 椭圆肋次通道微通道散热器热液性能空腔形状的数值研究

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972293

Senrong Liu, Zhong-liang Pan

{"title":"Numerical Study on Cavities Shape for the Hydrothermal Performance of Microchannel Heat Sinks with Elliptical Ribs and Secondary Channels","authors":"Senrong Liu, Zhong-liang Pan","doi":"10.1109/IConEEI55709.2022.9972293","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972293","url":null,"abstract":"In order to explore the effect of the different combinations of cavities, ribs, and secondary channels on the performance and efficiency of the microchannel heat sink (MCHS), this paper numerically investigated the heat transfer and fluid flow characteristics of MCHS with different shapes of cavities, elliptic ribs, and secondary channels, where the shapes of cavities include trapezoid, fan, elliptic, elliptic rectangle and triangle, named as MC-TraS, MC-FS, MC-ES, MC-ERS, MC-TriS, respectively. The results have verified that the overall performance of all the above radiators is better than that of smooth rectangular microchannels and the shape of cavities has an important effect on the pressure drop and the average temperature of the bottom surface. More specifically, compared with other optimized structures, MC-ERS has achieved the maximum system pressure drop but the highest overall heat dissipation performance with a maximum PEC of 1.73, while the MC-TriS has the minimum comprehensive performance with a minimum PEC of 1.26. In addition, there is little difference in heat dissipation performance between MC-FS and MC-ES.","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121047298","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: 0

Majority voting transfer learning CNN for peanut leaf types identification 多数投票迁移学习CNN用于花生叶类型识别

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972271

Nur Nafiiyah, Nenden Siti Fatonah, Retno Wardhani, Bambang Jokonowo, Taghfirul Azhima Yoga Siswa

{"title":"Majority voting transfer learning CNN for peanut leaf types identification","authors":"Nur Nafiiyah, Nenden Siti Fatonah, Retno Wardhani, Bambang Jokonowo, Taghfirul Azhima Yoga Siswa","doi":"10.1109/IConEEI55709.2022.9972271","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972271","url":null,"abstract":"In agricultural information systems, an effective way to identify plants is from their leaves because they have different characteristics and types, are easy, do not damage trees, and do not even need to be picked. Research related to the introduction of plant species using leaf-based CNN (Resnet101, ResNet50, ResNet18, MobileNet V2, DenseNet201, and GoogleNet) transfer learning has been carried out previously, but it is still not effective because of how to recognize peanut plant species using a single leaf and the accuracy value an average of 82.97. So this study proposes Majority Voting from the identification of the CNN transfer learning method to be able to effectively identify the type of bean based on the leaves of all the leaves on the stalk. The Majority Voting technique proposed is based on the type of class that is the most majority or dominant. Selection the proposed majority experimented with datasets of peanut leaf types, namely mung bean, soybean, long bean, and peanut leaves, a total dataset of 456 images of peanut leaves. Data collection is done by taking directly on the farmer’s land. The CNN transfer learning model used is Resnet101, ResNet50, ResNet18, MobileNet V2, DenseNet201, and GoogleNet. Results the Majority voting of proposed transfer learning has an accuracy of 96.93.","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130888555","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

Image Segmentation of Palm Leaf Pests to Determine Caterpillar Egg Populations Using Marker Watershed 基于标记分水岭的棕榈叶害虫图像分割识别毛虫卵种群

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972335

Ana Yulianti, Ause Labellapansa, H. Pertiwi, Sri Listia Rosa, M. Rizki Fadhilah, Octadino Haryadi

{"title":"Image Segmentation of Palm Leaf Pests to Determine Caterpillar Egg Populations Using Marker Watershed","authors":"Ana Yulianti, Ause Labellapansa, H. Pertiwi, Sri Listia Rosa, M. Rizki Fadhilah, Octadino Haryadi","doi":"10.1109/IConEEI55709.2022.9972335","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972335","url":null,"abstract":"Oil palm is an important industrial plant producing cooking oil, industrial oil, and fuel as call biodiesel. One of the factors that can cause a decrease in production yields on oil palm plants is pests. Palm oil companies through the Pest and Plant Diseases team prevent the breeding of pests by taking leaf samples first, for leaf sampling, they must carry out the stages of preparing an early observation schedule, determining sample points and sample lines, and determining sample subjects and it will take quite some time to get the results. Digital image processing is a method for processing digital images and producing other images according to their respective needs so that they are easily interpreted by humans and computers. Detection of caterpillar egg populations found in oil palm leaves is carried out using digital image processing, making it easier for the Pest and Plant Disease team to carry out direct detection of oil palm leaves to determine the caterpillar egg population and find solutions to handle caterpillar eggs. In this study, the population of caterpillar eggs contained in oil palm leaves was detected using the Watershed Marker-based Segmentation method. The stage of image processing begins with taking data obtained from one of the palm oil companies in Riau, then cropping is carried out and followed by color segmentation using Hue Saturation and Value by taking the Value score and then segmenting the marker watershed. The method of testing the credibility of the system uses the one feature method: single decision threshold. The results of testing the credibility of the system obtained a Sensitivity Value Percentage of 90.8% so that there were still 9.2% of the number of caterpillar eggs of oil palm leaf pests that had not been identified and the system accuracy was obtained at 89.4%.","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838955","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: 0

FPGA Based DCO With Fine Control Correlation Calibration Technique 基于FPGA的DCO精细控制相关校准技术

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972266

A. Almasoud, Mohammed Abbas, Mohammed Aboelola, A. Alghaihab

引用次数: 0

Applied Symbiotic Organisms Search Algorithm to solve Economic Emission Dispatch Problems 应用共生生物搜索算法求解经济排放调度问题

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972336

Thang Phan Van Hong, Tung Tran The

引用次数: 0

Hybrid Cooperative Spectrum Sensing Algorithm for Cognitive Radio Networks 认知无线电网络的混合协同频谱感知算法

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972303

M. Alfaqawi

{"title":"Hybrid Cooperative Spectrum Sensing Algorithm for Cognitive Radio Networks","authors":"M. Alfaqawi","doi":"10.1109/IConEEI55709.2022.9972303","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972303","url":null,"abstract":"Cognitive radio (CR) promises to tackle the challenge of spectrum scarcity by enabling spectrum sharing between secondary and primary users. In this regard, several spectrum sensing approaches have been proposed to maintain spectrum sharing without interfering the primary user (PU) activities. These approaches have different levels of computational complexity and accuracy. Despite the level of accuracy, none of the spectrum sensing approaches can overcome the hidden node problem. Therefore, this paper aims to tackle this problem by proposing a hybrid cooperative spectrum sensing (HCSS) algorithm. The proposed HCSS deploys the approaches of energy detection (ED) and cyclostationary feature detection (CFD) to detect the PU’s channel occupancy. Furthermore, OR-rule is deployed in order to minimize the interference of secondary users (SUs) on PU. The proposed HCSS is analyzed and compared against the non-cooperative case of ED and CFD and subject to AWGN and Rayleigh fading. The proposed HCSS algorithm is found to outperform ED and CFD in terms of probability of detection at various levels of signal-to-noise ratios (SNR).","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"24 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120927072","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: 0

A Method Comparison on Multi-Label Questions Classification for Assessment-Based Personalised Scaffolding Adaptive Learning Path 基于评估的个性化脚手架自适应学习路径多标签问题分类方法比较

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972269

Y. Wahyuningsih, A. Djunaidy, Daniel Oranova Siahaan

{"title":"A Method Comparison on Multi-Label Questions Classification for Assessment-Based Personalised Scaffolding Adaptive Learning Path","authors":"Y. Wahyuningsih, A. Djunaidy, Daniel Oranova Siahaan","doi":"10.1109/IConEEI55709.2022.9972269","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972269","url":null,"abstract":"Classification of the topic of a question item is one of the fundamental problems in e-learning systems. Unlike single-label classification, the multi-label classification method simultaneously predicts more than one-class label. This research is a series of process development for a Personal Diagnostic system based on assessment. This system needs annotated question bank because multi-label question items can be used to build a Concept Effect Relationship (CER). The purpose of building CER is to track the failed concept of students who fail the formative tests. Hence, there is necessary in looking for a multi-label question classification method. Therefore, this paper compares several multi-label classification methods in determining subject topics associated with questions in a formative test question bank. This study investigates the non-neural-based and neural-based multi-label classification. The test results for the non-neural show that Term Frequency– Inverse Document Frequency (TF-IDF) with Random Forest classifier produces the best hamming loss value (16,3%) while on neural, TF-IDF with convolutional neural network (CNN) produces a hamming loss value (21,2%) that is better than Long Short Term Memory (LSTM).","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124848636","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: 0

IoT-Based Monitoring and Control on Essential Oil Distillation Systems 基于物联网的精油蒸馏系统监测与控制

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972278

Syamsul Syamsul, S. Suherman, Sri Widianti

{"title":"IoT-Based Monitoring and Control on Essential Oil Distillation Systems","authors":"Syamsul Syamsul, S. Suherman, Sri Widianti","doi":"10.1109/IConEEI55709.2022.9972278","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972278","url":null,"abstract":"The focus of this research is the application of a control system on steam distillation in the process of refining essential oils. The control system uses a microcontroller-based PID method to get the efficiency of gas fuel consumption. In the distillation system used, heating is carried out using two energy sources, namely electricity to heat the heating element, and LPG gas. To improve the performance of the control system, IoT is added for inputting data on the characteristics of essential oils to be distilled in the refining process. Monitoring of temperature and pressure on steam distillation is delivered periodically. Temperature monitoring is also carried out on the distillation cooling system. The efficiency of gas fuel consumption is obtained by applying the PID control method, namely the temperature is set in the range of 100°C -120°C, and the pressure is set in the range of 1 atm. The results obtained from the application of the PID control method with IoT-based monitoring are gas fuel efficiency. For the refining of nutmeg essential oil, the efficiency obtained is 17%, for 9 hours of the distillation process. And in the patchouli essential oil refining process, the efficiency of using LPG gas is 14% for 8 hours of the refining process. For citronella essential oil distillation, the efficiency of using LPG gas is 6%, for 5 hours of the distillation process. The yield of nutmeg essential oil was obtained at 5% for 9 hours of distillation. Patchouli essential oil yield was obtained 1.30% for 8 hours of distillation. And the yield of citronella essential oil was obtained at 1.1% for 5 hours of distillation time.","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125070977","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: 0

A Comparative Study on Machine Learning based Prediction Models for Public Participation Rate in an Election Voting 基于机器学习的选举投票公众参与率预测模型比较研究

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI) Pub Date : 2022-10-19 DOI: 10.1109/IConEEI55709.2022.9972283

A. Fitrani, Nikko Enggaliano Pratama, A. B. Raharjo, Yudhi Purwananto, D. Purwitasari

{"title":"A Comparative Study on Machine Learning based Prediction Models for Public Participation Rate in an Election Voting","authors":"A. Fitrani, Nikko Enggaliano Pratama, A. B. Raharjo, Yudhi Purwananto, D. Purwitasari","doi":"10.1109/IConEEI55709.2022.9972283","DOIUrl":"https://doi.org/10.1109/IConEEI55709.2022.9972283","url":null,"abstract":"Prediction of public participation in elections is one measure of election success. Voter participation is at the polling station level and involves four data sources: voters, polling stations, recapitulation, and village profiles. The preprocessing stage is carried out on each dataset, including maintenance, transformation, and integration. Two types of datasets are defined, involving all attributes and removing the result from attribute correlation. Classification method with five machine learning algorithms (ML) with participation prediction classes labelled High and Low. The highest result is 85.90% for the type 1 dataset and Artificial Neural Network (ANN) algorithm with 60% training and 40% testing split dataset. Furthermore, for detachment type 2, by eliminating several attributes, 100% results are obtained for the K-Nearest Neighbor (kNN) algorithm with a split dataset of 70% training and 30% testing. Of the five ML algorithms, only the Naïve Bayes (NB) algorithm did not experience an increase in prediction results. Furthermore, the significant influence of attributes on the prediction class is shown in each attribute of the dataset, including the Permanent Voter List at the Polling Place (DPT TPS), Local, Health Access and Total Recapitulation.","PeriodicalId":382763,"journal":{"name":"2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124470558","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: 0