Xiaodong Liu¹, Qi Liu^1,*

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 179-194, 2018, DOI:10.32604/cmc.2018.04025

Abstract In a home energy management system (HEMS), appliances are becoming diversified and intelligent, so that certain simple maintenance work can be completed by appliances themselves. During the measurement, collection and transmission of electricity load data in a HEMS sensor network, however, problems can be caused on the data due to faulty sensing processes and/or lost links, etc. In order to ensure the quality of retrieved load data, different solutions have been presented, but suffered from low recognition rates and high complexity. In this paper, a validation and repair method is presented to detect potential failures and errors in a domestic… More >

Baojia Wang¹, Pingzeng Liu^1,*, Zhang Chao¹, Wang Junmei¹, Weijie Chen¹, Ning Cao², Gregory M.P. O’Hare³, Fujiang Wen¹

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 283-296, 2018, DOI:10.32604/cmc.2018.03791

Abstract Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict the nonlinear part of garlic… More >

Xiaoping Zhao¹, Jiaxin Wu^1,*, Yonghong Zhang², Yunqing Shi³, Lihua Wang²

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 223-242, 2018, DOI:10.32604/cmc.2018.02490

Abstract With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform are used as input to… More >

Xi Kan¹, Yonghong Zhang^2,*, Linglong Zhu², Liming Xiao², Jiangeng Wang³, Wei Tian⁴, Haowen Tan⁵

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 49-68, 2018, DOI:10.32604/cmc.2018.02376

Abstract Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow, cloud and snow-free land. Moreover,… More >

Shuren Zhou^1,2,*, Wenlong Liang^1,2, Junguo Li^1,2, Jeong-Uk Kim³

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >

Huiyu Sun^1,*, Suzanne McIntosh¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 1-9, 2018, DOI:10.32604/cmc.2018.03684

Abstract The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while… More >

Yuyu Chen¹, Bangxu Yin², Hongjie He², Shu Yan², Fan Chen^2,*, Hengming Tai³

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 299-312, 2018, DOI: 10.3970/cmc.2018.03179

Abstract To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in… More >

Lizhi Xiong^1,*, Yunqing Shi²

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 523-539, 2018, DOI: 10.3970/cmc.2018.01791

Abstract Advanced cloud computing technology provides cost saving and flexibility of services for users. With the explosion of multimedia data, more and more data owners would outsource their personal multimedia data on the cloud. In the meantime, some computationally expensive tasks are also undertaken by cloud servers. However, the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data. Recently, this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data. In this paper, two reversible data hiding schemes are proposed for encrypted… More >

Yang Du¹, Zhaoxia Yin^1,2,*, Xinpeng Zhang³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 495-507, 2018, DOI: 10.3970/cmc.2018.02440

Abstract This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification. The most in JPEG bitstream consists of a sequence of VLCs (variable length codes) and the appended bits. Each VLC has a corresponding RLV (run/length value) to record the AC/DC coefficients. To achieve lossless data hiding with high payload, we shift the histogram of VLCs and modify the DHT segment to embed data. Since we sort the histogram of VLCs in descending order, the filesize expansion is limited. The paper’s key contribution includes: Lossless data hiding, less filesize expansion in identical pay-load and… More >

Yangyang Wang¹, Rongrong Ni^1,*, Yao Zhao¹, Min Xian²

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 255-265, 2018, DOI:10.3970/cmc.2018.01732

Abstract In this paper, two methods are proposed to embed visual watermark into direct binary search (DBS) halftone images, which are called Adjusted Direct Binary Search (ADBS) and Dual Adjusted Direct Binary Search (DADBS). DADBS is an improved version of ADBS. By using the proposed methods, the visual watermark will be embedded into two halftone images separately, thus, the watermark can be revealed when these two halftone images are overlaid. Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns. More >