IEICE Trans. Fundam. Electron. Commun. Comput. Sci.最新文献_第3页

Encrypted Traffic Categorization Based on Flow Byte Sequence Convolution Aggregation Network 基于流字节序列卷积聚合网络的加密流量分类

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-07-01 DOI: 10.1587/transfun.2020eal2102

Lin Yan, Mingyong Zeng, Shuaimin Ren, Zhangkai Luo

引用次数: 0

Kernel Weights for Equalizing Kernel-Wise Convergence Rates of Multikernel Adaptive Filtering 均衡多核自适应滤波收敛速率的核权算法

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020eap1080

Kwangjin Jeong, M. Yukawa

引用次数: 1

Domain Adaptive Cross-Modal Image Retrieval via Modality and Domain Translations 基于模态和域转换的域自适应跨模态图像检索

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020imp0011

Rintaro Yanagi, Ren Togo, Takahiro Ogawa, M. Haseyama

引用次数: 0

A Circuit Analysis of Pre-Emphasis Pulses for RC Delay Lines RC延迟线预强化脉冲电路分析

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020eap1083

Kazuki Matsuyama, T. Tanzawa

引用次数: 2

Occurrence Prediction of Dislocation Regions in Photoluminescence Image of Multicrystalline Silicon Wafers Using Transfer Learning of Convolutional Neural Network 基于卷积神经网络迁移学习的多晶硅片光致发光图像中位错区出现预测

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020imp0010

H. Kudo, T. Matsumoto, K. Kutsukake, N. Usami

{"title":"Occurrence Prediction of Dislocation Regions in Photoluminescence Image of Multicrystalline Silicon Wafers Using Transfer Learning of Convolutional Neural Network","authors":"H. Kudo, T. Matsumoto, K. Kutsukake, N. Usami","doi":"10.1587/transfun.2020imp0010","DOIUrl":"https://doi.org/10.1587/transfun.2020imp0010","url":null,"abstract":"In this paper, we evaluate a prediction method of regions including dislocation clusters which are crystallographic defects in a photoluminescence (PL) image of multicrystalline silicon wafers. We applied a method of a transfer learning of the convolutional neural network to solve this task. For an input of a sub-region image of a whole PL image, the network outputs the dislocation cluster regions are included in the upper wafer image or not. A network learned using image in lower wafers of the bottom of dislocation clusters as positive examples. We experimented under three conditions as negative examples; image of some depth wafer, randomly selected images, and both images. We examined performances of accuracies and Youden’s J statistics under 2 cases; predictions of occurrences of dislocation clusters at 10 upper wafer or 20 upper wafer. Results present that values of accuracies and values of Youden’s J are not so high, but they are higher results than ones of bag of features (visual words) method. For our purpose to find occurrences dislocation clusters in upper wafers from the input wafer, we obtained results that randomly select condition as negative examples is appropriate for 10 upper wafers prediction, since its results are better than other negative examples conditions, consistently. key words: prediction, transfer learning, convolutional neural network","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"104-A 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784462","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

Analysis and Design of Aggregate Demand Response Systems Based on Controllability 基于可控性的总需求响应系统分析与设计

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020eap1093

Kazuhiro Sato, S. Azuma

{"title":"Analysis and Design of Aggregate Demand Response Systems Based on Controllability","authors":"Kazuhiro Sato, S. Azuma","doi":"10.1587/transfun.2020eap1093","DOIUrl":"https://doi.org/10.1587/transfun.2020eap1093","url":null,"abstract":"We address analysis and design problems of aggregate demand response systems composed of various consumers based on controllability to facilitate to design automated demand response machines that are installed into consumers to automatically respond to electricity price changes. To this end, we introduce a controllability index that expresses the worst-case error between the expected total electricity consumption and the electricity supply when the best electricity price is chosen. The analysis problem using the index considers how to maximize the controllability of the whole consumer group when the consumption characteristic of each consumer is not fixed. In contrast, the design problem considers the whole consumer group when the consumption characteristics of a part of the group are fixed. By solving the analysis problem, we first clarify how the controllability, average consumption characteristics of all consumers, and the number of selectable electricity prices are related. In particular, the minimum value of the controllability index is determined by the number of selectable electricity prices. Next, we prove that the design problem can be solved by a simple linear optimization. Numerical experiments demonstrate that our results are able to increase the controllability of the overall consumer group. key words: aggregate demand response, controllability, real-time pricing","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826558","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

Hyperspectral Image Denoising Using Tensor Decomposition under Multiple Constraints 基于多约束张量分解的高光谱图像去噪

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020eal2099

Zhen Li, Baojun Zhao, Wenzheng Wang, Baoxian Wang

引用次数: 0

Scene Adaptive Exposure Time Control for Imaging and Apparent Motion Sensor 成像与视动传感器的场景自适应曝光时间控制

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020iml0007

Misaki Shikakura, Yusuke Kameda, T. Hamamoto

{"title":"Scene Adaptive Exposure Time Control for Imaging and Apparent Motion Sensor","authors":"Misaki Shikakura, Yusuke Kameda, T. Hamamoto","doi":"10.1587/transfun.2020iml0007","DOIUrl":"https://doi.org/10.1587/transfun.2020iml0007","url":null,"abstract":"CMOS image sensors have been developed for surveillance and industrial equipment cameras. In these applications, it is important to capture images with clear details for image recognition and object tracking. Since the exposure parameters (including exposure time, which is examined in this paper) are not appropriate, overexposure and underexposure may occur when the illuminance varies with artificial or natural light. Therefore, it is necessary to capture an image with the exposure time that is adjusted to the illuminance level of the scene. At the same time, motion blur must be suppressed when the camera or subject is moving. Many auto exposure algorithms adopt the average brightness value of the scene to control the exposure time. The method based on the brightness value in [1] adjust the exposure time for the important object such as a moving object. The weight of the segmented moving object is higher through object tracking, and control the proper exposure time for moving objects. However, this approach probably cause motion blur When the moving subject is dark and exposure time is set long. Imaging with a short exposure time can suppress motion blur, and the exposure time can be estimated by using motion estimation. High frame rate imaging is effective for improving the accuracy of motion estimation [2], [3]. The correlation between frames at high frame rate is so high that the computational complexity of motion estimation is reduced. However, many frames are needed to output from the image sensor to a signal processing circuit outside the sensor, and imaging at high frame rate increases the data rate. Several methods [4]–[7] mount a simple processing circuit","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"542 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116602","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

Video Smoke Removal from a Single Image Sequence 从单个图像序列的视频烟雾去除

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020imp0013

Shiori Yamaguchi, K. Hirai, T. Horiuchi

{"title":"Video Smoke Removal from a Single Image Sequence","authors":"Shiori Yamaguchi, K. Hirai, T. Horiuchi","doi":"10.1587/transfun.2020imp0013","DOIUrl":"https://doi.org/10.1587/transfun.2020imp0013","url":null,"abstract":"In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure. key words: moving camera, smoke imaging model, smoke removal, spatiotemporal pixel compensation, video processing","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"104-A 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131379905","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

Occlusion Avoidance Behavior During Gazing at a Rim Drawn by Blue-Yellow Opposite Colors 注视由蓝黄对比色绘制的边缘时的避光行为

IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Pub Date : 2021-06-01 DOI: 10.1587/transfun.2020iml0001

Miho Shinohara, Y. Tamura, Shinya Mochiduki, H. Kudo, M. Yamada

引用次数: 0