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2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)最新文献

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Complexity Metric of Infrared Image for Automatic Target Recognition 用于自动目标识别的红外图像复杂度度量
Xiaotian Wang, Wan-chao Ma, Kai Zhang, Jie Yan
Image complexity metric is an important part of automatic target recognition(ATR) performance evaluation, the relationship between infrared image complexity metric and target recognition is studied, which is important for infrared imaging system performance prediction and evaluation and the performance comparison of target recognition algorithms. Aiming at this problem, an automatic target recognition infrared image complexity metric method is proposed. Firstly, the infrared imaging mechanism is analyzed to find the main factors affecting target recognition. The image complexity is defined from the similarity degree of target and clutter and the submergence degree of target and clutter, which clarify for the influence of target recognition. To increase the universality of image complexity, the concept of feature space was introduced. Finally, the weighted processing and statistical formula F1-Score is used to combine the three indexes, the complexity of the frame image is established. The experimental results show that the proposed metric is more valid than traditional metrics, such as SV and SCR, has a strong correlation with automatic target recognition algorithm, while the values are in better agreement with the actual situation.
图像复杂度度量是自动目标识别(ATR)性能评价的重要组成部分,研究红外图像复杂度度量与目标识别的关系,对红外成像系统的性能预测与评价以及目标识别算法的性能比较具有重要意义。针对这一问题,提出了一种红外图像自动目标识别复杂度度量方法。首先,分析红外成像机理,找出影响目标识别的主要因素。从目标与杂波的相似程度和目标与杂波的淹没程度来定义图像复杂度,明确了对目标识别的影响。为了提高图像复杂度的普适性,引入了特征空间的概念。最后,利用加权处理和统计公式F1-Score对三个指标进行组合,建立帧图像的复杂度。实验结果表明,该度量比传统的SV、SCR等度量更有效,与自动目标识别算法有较强的相关性,且数值更符合实际情况。
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引用次数: 2
A CNN-Based Computational Encoding Model for Human V2 Cortex 基于cnn的人类V2皮层计算编码模型
Yicong Hu, Kai Qiao, Linyuan Wang, Li Tong, Chi Zhang, Hui Gao, Bin Yan
The computation encoding models, used to predict human brain activity from natural image stimuli, can be performed as a function simulator of human vision information process. In the traditional computational encoding models for human V2 cortex, due to the lack of higher visual feature and information processing hierarchy, it is difficult to achieve expected predict performance. Here, activated by the properties of CNN, we trained a CNN as an encoding model for human V2 cortex, which can be trained for predicting stimuli-evoked response measured by functional magnetic resonance imaging. The results reveal that the CNN-based encoding model can achieve a higher performance, proves that CNN have advantages in encoding higher visual areas. This finding provides a new framework for the human vision encoding models and helps to further understand of the human vision mechanism from the computational point view.
计算编码模型用于从自然图像刺激中预测人脑活动,可以作为人类视觉信息处理的功能模拟器。在传统的人类V2皮层计算编码模型中,由于缺乏更高的视觉特征和信息处理层次,难以达到预期的预测性能。在此,利用CNN的特性激活,我们训练了一个CNN作为人类V2皮层的编码模型,该模型可以用于预测功能磁共振成像测量的刺激诱发反应。结果表明,基于CNN的编码模型可以达到更高的性能,证明CNN在编码更高视觉区域方面具有优势。这一发现为人类视觉编码模型提供了一个新的框架,有助于从计算的角度进一步理解人类视觉机制。
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引用次数: 0
A Bio Inspired Hybrid Krill Herd-Extreme Learning Machine Network Based on LBP and GLCM for Brain Cancer Tissue Taxonomy 基于LBP和GLCM的生物启发杂交磷虾群-极限学习机网络用于脑癌组织分类
J. Preethi
Brain cancers are the second most common disease in children. The radiologist plays a vital role in diagnosing a disease. Manual classification is a time consuming process and can cause human errors. Our objective is to develop a fully automated classification method for identification of brain cancers. Methods: This paper proposes a Bio Inspired Hybrid Krill Herd-Extreme Learning Machine (ELM) Network which classifies the Brain images into one of the classes namely normal image, Astrocytoma cancer, Meningioma cancer or Oligidendroglioma cancer. The most essential part of the research is to find the local and global features from the brain cancer images. In this proposed method, both Local Binary Patterns (LBP) and Gray Level Co-occurrence Matrix (GLCM) features are used for feature extraction. The real time brain database is obtained from Jansons MRI Diagnostic centre Erode during November 1, 2013 to December 31, 2014 consisting of 400 images with their ages ranging from 20 to 65 years. In our experiment, 85 samples aretaken for training and the remaining 15 samples are taken for testing. Initially, the local feature information is extracted using LBP method and the overall global features are extracted using GLCM method. By these methods, the brain images are fully illustrated using local and global features. Then the statistical technique is used for feature sub selection where the variance of each features are calculated. The selected features from statistical technique is fed as inputs to the ELM Neural Network classifier where the weights are optimized using Krill Herd algorithm.Results: This proposed hybrid approach achieves 98.9% accuracy when compared with other traditional techniques.
脑癌是儿童中第二大常见疾病。放射科医生在诊断疾病方面起着至关重要的作用。手动分类是一个耗时的过程,并且可能导致人为错误。我们的目标是开发一种全自动识别脑癌的分类方法。方法:提出了一种生物启发杂交磷虾群-极限学习机(ELM)网络,该网络将脑图像分为正常图像、星形细胞瘤癌、脑膜瘤癌和少突胶质细胞瘤癌。从脑癌图像中寻找局部特征和全局特征是研究的关键。该方法利用局部二值模式(LBP)和灰度共生矩阵(GLCM)特征进行特征提取。2013年11月1日至2014年12月31日期间,由400张年龄从20岁到65岁不等的图像组成的实时大脑数据库从Jansons MRI诊断中心获得。在我们的实验中,85个样本用于训练,剩下的15个样本用于测试。首先使用LBP方法提取局部特征信息,然后使用GLCM方法提取整体特征。通过这些方法,可以充分利用局部和全局特征来描述大脑图像。然后利用统计技术进行特征子选择,计算每个特征的方差。从统计技术中选择的特征作为ELM神经网络分类器的输入,使用Krill Herd算法对权重进行优化。结果:与其他传统方法相比,该方法的准确率达到98.9%。
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引用次数: 3
Clustering Method for Financial Time Series with Co-Movement Relationship 具有共动关系的金融时间序列的聚类方法
Jungyu Ahn, Ju-hong Lee
Due to the random walk property of the financial time series, it is very difficult to develop a system that solves real financial application problems. However, if we obtain a time series cluster with a high degree of co-movement, it will be very useful for developing financial application systems. This paper proposes a clustering method that finds time series clusters with higher degrees of co-movement than the existing time series clustering algorithms. There is a problem in that clusters generated by the existing time series clustering algorithms contain too much noise with a low degree of co-movement. We propose a clustering method that solves the problem. This method is performed in the following steps. In the Data Preprocessing step, it performs Average Scaling, Weighted Time Series Transformation, Dimension Reduction, and Cluster Diameter Estimation. In the Clustering Step, it performs Preclustering and Refinement. Experiments show that our clustering method has higher performance than the existing time series clustering algorithms in finding clusters with high degree of co-movement.
由于金融时间序列的随机游走特性,开发一个能够解决实际金融应用问题的系统是非常困难的。然而,如果我们得到一个具有高度协同运动的时间序列簇,它将对开发金融应用系统非常有用。本文提出了一种寻找比现有时间序列聚类算法具有更高共动度的时间序列聚类的聚类方法。现有的时间序列聚类算法产生的聚类存在噪声过多、共运动程度低的问题。我们提出了一种聚类方法来解决这个问题。该方法通过以下步骤执行。在数据预处理步骤中,它执行平均缩放、加权时间序列变换、降维和聚类直径估计。在聚类步骤中,它执行预聚类和细化。实验表明,我们的聚类方法在寻找高度共同运动的聚类方面比现有的时间序列聚类算法具有更高的性能。
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引用次数: 1
Text Extraction and Categorization from Watermark Scientific Document in Bulk 大规模水印科学文献的文本提取与分类
Wai Chong Chia, P. Teh, C. M. Gill
Extracting information from a large number of scientific documents prepared in portable document format (PDF) is a time-consuming process, if all this is to be done without the help of an automated system. However, the missing of structural information in PDF can create a lot of issues during the extraction process. Watermark is one of the objects that can have a negative effect on this. When PDF extraction tool is applied to PDF with watermark, the watermark can affect the order of the text and is often extracted as part of the text. If the text is to be used for analysis in the future, the watermark might affect the accuracy in the results, since they should not be taken into consideration. In this paper, an approach that can be used to overcome the issue above is proposed. The proposed approach makes use of direct text recognition from PDF and optical character recognition (OCR) to produce two version of digital text that can be combined for better accuracy. The results shown that the proposed approach is capable of extracting text from PDF with different watermark patterns.
如果所有这些都是在没有自动化系统的帮助下完成的,那么从大量以便携文件格式(PDF)准备的科学文件中提取信息是一个耗时的过程。然而,PDF中结构信息的缺失会在提取过程中产生很多问题。水印是一种可以对其产生负面影响的对象。当PDF提取工具应用于带水印的PDF时,水印会影响文本的顺序,通常作为文本的一部分被提取出来。如果文本将来用于分析,水印可能会影响结果的准确性,因为它们不应该被考虑在内。在本文中,提出了一种可以用来克服上述问题的方法。该方法利用PDF的直接文本识别和光学字符识别(OCR)来生成两个版本的数字文本,可以将它们组合在一起以提高准确性。结果表明,该方法能够从具有不同水印模式的PDF文件中提取文本。
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引用次数: 0
Optimization Model of Ready-Mix Concrete Delivery Route and Schedule: A Case in Indonesia RMC Industry 预拌混凝土运输路线与进度优化模型——以印尼RMC行业为例
R. Syahputra, K. Komarudin, A. R. Destyanto
The focus of national development on the infrastructure sector impacts on the rapid growth of the construction market. High demand and complex business processes make ready-mix concrete producers especially in Jakarta no longer able to rely on route planning and manual scheduling mechanisms, which have been some delays in deliveries that impact on the decline in service level. This research proposes an optimization method based on mixed integer linear programming on route planning mechanism and scheduling of ready-mix concrete delivery developed in Java language with Gurobi optimization library support. The simulation is done using the companys historical data of the research object, which is one of the ready-mix concrete producers in Jakarta. From the four simulations, the best output resulted with a total cost of - 3674 and a gap of 0.49%, where all customer requests are met in the given time window. These results indicate that the optimization model developed in this study can yield the optimum solution for route planning mechanism and ready-mix concrete delivery scheduling.
国家对基础设施部门的发展重点影响了建筑市场的快速增长。高需求和复杂的业务流程使得预拌混凝土生产商,特别是雅加达的生产商,不再能够依靠路线规划和人工调度机制,这在一定程度上延迟了交货,影响了服务水平的下降。本研究提出了一种基于混合整数线性规划的预拌混凝土输送路线规划机制和调度优化方法,该方法采用Java语言开发,支持ruby优化库。仿真是利用研究对象雅加达某预拌混凝土生产企业的历史数据进行的。从四个模拟中,最佳输出结果是总成本为- 3674,差距为0.49%,其中所有客户请求都在给定的时间窗口内得到满足。研究结果表明,所建立的优化模型能够给出路线规划机制和预拌混凝土配送调度的最优解。
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引用次数: 2
A Study of Non-Normal Process Capability Analysis Based on Box-Cox Transformation 基于Box-Cox变换的非正常过程能力分析研究
Yanming Yang, Huayuan Zhu
Process capability indices are the important tools used in most of the manufacturing industries to check whether the manufactured products meet their quality specifications or not. Process capability analysis requires that the quality characteristic data be normally distributed. In actual production, a lot of stable processes do not necessarily satisfy the assumption of normal distribution. An approach to tackle this problem is to use the appropriate transformation methods to convert these non-normal data. Therefore, a method of converting non-normal data into normal data is proposed so that the data can be analyzed using the process capability indices. In this paper, an improved Box-Cox transformation model is proposed to deal with non-normal data and calculate its process capability indices, and the concrete steps are given. Finally, the method is used to study the actual cases, and the process capability indices are calculated. The effectiveness and practicability of the method are proved by comparison with the actual situation. In this paper, Minitab analysis software is used to assist the realization of this method. It has strong operability and convenience, and can be used to guide production practice.
在大多数制造业中,过程能力指标是检验制造产品是否符合其质量规范的重要工具。过程能力分析要求质量特征数据为正态分布。在实际生产中,许多稳定过程并不一定满足正态分布的假设。解决这一问题的方法是使用适当的转换方法对这些非正态数据进行转换。为此,提出了一种将非正态数据转换为正态数据的方法,以便利用过程能力指标对数据进行分析。本文提出了一种改进的Box-Cox变换模型来处理非正态数据并计算其处理能力指标,并给出了具体步骤。最后,将该方法应用于实际案例研究,计算了过程能力指标。通过与实际情况的比较,证明了该方法的有效性和实用性。本文使用Minitab分析软件辅助实现该方法。具有较强的可操作性和方便性,可用于指导生产实践。
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引用次数: 4
ICCIA 2018 Reviewers
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引用次数: 0
Title Page i 第1页
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引用次数: 0
Area Constraint Aware Physical Unclonable Function for Intelligence Module 智能模块的区域约束感知物理不可克隆功能
Y. Nozaki, M. Yoshikawa
Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.
神经网络(NN)等人工智能技术被广泛应用于物联网智能模块中。另一方面,指出了物联网设备遭受非法攻击的风险;因此,认证等安全对策非常重要。在硬件安全领域,物理不可克隆功能(PUFs)作为防止半导体仿冒品的认证技术受到了广泛的关注。然而,对于神经网络和PUF的专用硬件的实现增加了电路面积。因此,本研究提出了一种新的智能模块区域约束感知PUF。所提出的PUF利用了神经网络从输入层到输出层的传播延迟时间。为了实现元器件共享,PUF减小了电路的面积。实验使用现场可编程门阵列评估电路面积和PUF性能。电路面积结果表明,所提出的PUF比传统的PUF要小。在PUF性能评价中,从稳定性、弥漫性、唯一性三个方面进行了较好的评价。
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引用次数: 3
期刊
2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)
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