IEICE Transactions on Information and Systems

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ISSN / EISSN : 0916-8532 / 1745-1361
Total articles ≅ 6,207
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Weiguo Zhang, Jiaqi Lu, Jing Zhang, Xuewen Li, Qi Zhao
IEICE Transactions on Information and Systems, pp 1085-1093;

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.
Tomoya Hashiguchi, Takehiro Yamamoto, Sumio Fujita, Hiroaki Ohshima
IEICE Transactions on Information and Systems, pp 928-935;

In this study, we generate dialogue contents in which two systems discuss their distress with each other. The user inputs sentences that include environment and feelings of distress. The system generates the dialogue content from the input. In this study, we created dialogue data about distress in order to generate them using deep learning. The generative model fine-tunes the GPT of the pre-trained model using the TransferTransfo method. The contribution of this study is the creation of a conversational dataset using publicly available data. This study used EmpatheticDialogues, an existing empathetic dialogue dataset, and Reddit r/offmychest, a public data set of distress. The models fine-tuned with each data were evaluated both automatically (such as by the BLEU and ROUGE scores) and manually (such as by relevance and empathy) by human assessors.
Hao Fang, , Dewang Chen, Feng-Jang Hwang
IEICE Transactions on Information and Systems, pp 1112-1115;

Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.
Byungjae Park
IEICE Transactions on Information and Systems, pp 1116-1119;

This letter proposes a post-processing method to improve the smoothness and safety of the path for an autonomous vehicle navigating in an urban environment. The proposed method transforms the initial path given by local path planning algorithms using a stochastic approach to improve its smoothness and safety. Using the proposed method, the initial path is efficiently transformed by iteratively updating the position of each waypoint within it. The proposed method also guarantees the feasibility of the transformed path. Experimental results verify that the proposed method can improve the smoothness and safety of the initial path and ensure the feasibility of the transformed path.
Tongzhou Qu, Zibin Dai, Yanjiang Liu, Lin Chen, Xianzhao Xia
IEICE Transactions on Information and Systems, pp 964-972;

The existing research on Amdahl's law is limited to multi/many-core processors, and cannot be applied to the important parallel processing architecture of coarse-grained reconfigurable arrays. This paper studies the relation between the multi-level parallelism of block cipher algorithms and the architectural characteristics of coarse-grain reconfigurable arrays. We introduce the key variables that affect the performance of reconfigurable arrays, such as communication overhead and configuration overhead, into Amdahl's law. On this basis, we propose a performance model for coarse-grain reconfigurable block cipher array (CGRBA) based on the extended Amdahl's law. In addition, this paper establishes the optimal integer nonlinear programming model, which can provide a parameter reference for the architecture design of CGRBA. The experimental results show that: (1) reducing the communication workload ratio and increasing the number of configuration pages reasonably can significantly improve the algorithm performance on CGRBA; (2) the communication workload ratio has a linear effect on the execution time.
Fei Zhang, Peining Zhen, Dishan Jing, Xiaotang Tang, Hai-Bao Chen, Jie Yan
IEICE Transactions on Information and Systems, pp 1024-1038;

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.
Sejin Jung, Eui-Sub Kim, Junbeom Yoo
IEICE Transactions on Information and Systems, pp 1103-1106;

Traditional safety analysis techniques have shown difficulties in incorporating dynamically changing structures of CPSs (Cyber-Physical Systems). STPA (System-Theoretic Process Analysis), one of the widely used, needs to unfold and arrange all hidden structures before beginning a full-fledged analysis. This paper proposes an intermediate model “Information Unfolding Model (IUM)” and a process “Information Unfolding Process (IUP)” to unfold dynamic structures which are hidden in CPSs and so help analysts construct control structures in STPA thoroughly.
Rizal Setya Perdana, Yoshiteru Ishida
IEICE Transactions on Information and Systems, pp 873-886;

This study presents a formulation for generating context-aware natural language by machine from visual representation. Given an image sequence input, the visual storytelling task (VST) aims to generate a coherent, object-focused, and contextualized sentence story. Previous works in this domain faced a problem in modeling an architecture that works in temporal multi-modal data, which led to a low-quality output, such as low lexical diversity, monotonous sentences, and inaccurate context. This study introduces a further improvement, that is, an end-to-end architecture, called cross-modal contextualize attention, optimized to extract visual-temporal features and generate a plausible story. Visual object and non-visual concept features are encoded from the convolutional feature map, and object detection features are joined with language features. Three scenarios are defined in decoding language generation by incorporating weights from a pre-trained language generation model. Extensive experiments are conducted to confirm that the proposed model outperforms other models in terms of automatic metrics and manual human evaluation.
Haoyu Xu, Yuenan Li
IEICE Transactions on Information and Systems, pp 1125-1129;

In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.
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