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Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold d, the storage cost will grow exponentially or the precision of search results will greatly decrease as $d$ increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this paper, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over the encrypted database, in which our scheme achieves fuzzy keyword search for any similarity threshold d with constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

The physical and chemical processes of surface discharge are more complicated than other discharge forms due to the influence of gas composition and dielectric properties. Spectral diagnosis can be used to analyze the main species in the discharge process. However, the chromaticity characteristics are the comprehensive representation of all emission spectra. This article presents a new method to diagnose the state of surface discharge using chromaticity coordinates in color space. It is observed that the chromaticity coordinates slide along a straight line between the equal energy white point and a specific spectral color with the development of surface discharge. The relative position of the chromaticity coordinates on the line indicates the different states of the surface discharge. The slope of the line is affected by the material characteristics while external factors such as the camera’s exposure time and the color of materials have no effect on it. It is found that the slope of the chromaticity coordinate curve has a fixed range, which means that there exists a specific color space of surface discharge. Finally, the effects of the ambient noise are corrected. This method uses the chromaticity coordinates in a long time scale to diagnose the microscopic process of surface discharge and it can be extended to other state diagnoses of partial discharge phenomena. From the point of view of potential application, this method also provides a new simple method to meet the requirement of long time scale state monitoring in industrial application.

To solve the problems of low resolution, weak antinoise performance, and imprecise fault location in discharge recognition of ultraviolet imaging, an intelligent state recognition method of surface discharge based on visible images and machine learning (ML) is proposed in this article. A visible image library of surface discharge (ac) under different experimental conditions was established, and the chromatic, gray-scale, and morphological features of images were extracted. The image library was divided into four stages by clustering. Combined with spectrum correlation experiments, the physical meaning of the division results was explained. Supervised learning algorithms, including classic algorithms and deep learning, were used to train intelligent recognition models. The results showed that models based on chromatic features had a higher accuracy for recognition; the accuracy of artificial neural networks reached 0.982, which was obviously higher than that of classical learning algorithms (0.886). The results show that visible images can be used for the state recognition of surface discharges effectively. The application of ML ensures high efficiency and accuracy. This method will enable state recognition and fault location to be completed simultaneously.

The noncontact diagnosis methods of discharge plasma include spectrum, electromagnetic wave, ultraviolet image, and high-speed nanosecond image analyses, but there are few studies on the visible image diagnosis method. With the development of digital cameras, the number of pixels in a visible image is far greater than that of ultraviolet, infrared, and high-speed cameras; the image contains a considerable amount of discharge spatial information and provides a new solution for plasma diagnosis. We obtained visible digital images of short gap negative dc coronas with the corresponding oscilloscope current signals under the experimental conditions of atmospheric pressure and room temperature. Two colorimetric indicators ( RGB and HSI ) were proposed to study the colorimetry features of discharge images, of which the analysis was correlated with the amplitude and frequency of oscilloscope current signals. The result shows that the hue value of the overall image region is positively related to the pulse amplitude, while the intensity value corresponds to the pulse frequency. The stage division based on the hue value is better than that based on current pulses, which shows that the Trichel stage can be further subdivided. In addition, we observed that the anode discharge begins earlier than the formation of pulseless glow, and we illustrated that anode discharge played an important role in the formation of pulseless glow and spark breakdown. Briefly, the discharge image corresponds well with the results of traditional detection methods and provides a new perspective for the corona discharge study, deserving further research.

Can we detect electric discharge states in gases based on the information on visual images? This article proposes a new kind of method where we build several detection models for different states of corona discharge by applying four kinds of machine learning algorithms to extract color, brightness, and shape information characteristics of visible images taken by a digital camera. Every model is then tested on a new set of images to measure its performance. The four different machine learning algorithms are support vector machine (SVM), ${K}$ -nearest neighbor regression (KNN), single layer perceptron (SLP), and decision tree (DT) algorithms. The prediction results show that the color features perform best among all three types of features and the KNN algorithm performs best among all four algorithms. This article also presents a discussion on how to choose the optimal detection areas of images for better detection performance. Our approach shows consistent results across different cameras and camera settings. The results demonstrate that even if only the visible light spectrum emitted from a plasma is captured, the color method can provide sufficient discharge information for economic and convenient use in discharge state detection because the species producing visible radiation are affected by radiation in all bands.

It is common for researchers to learn about the physical process of discharge by studying the intensity of specific spectral lines in the emission spectrum. By using this method, every microscopic process involving light radiation can be quantitatively analyzed, but there is a problem of how to select appropriate spectral lines for the comprehensive judgment of changes in the discharge process. Here, we present a comprehensive method for converting the visible spectrum of discharge into chromaticity coordinates. In this way, a large number of spectral data are transformed into a single chromaticity coordinate to diagnose the gas discharge directly and quickly, and the comprehensive evaluation of the discharge status is implemented.

Secure multi-keyword ranked search over outsourced cloud data has become a hot research field. Most existing works follow the model of “Single Owner”, which just supports searching on the outsourced data belong to only one data owner. But the more realistic scenario is “Multiple Owners”, users could search on all datasets outsourced by different data owners. However, directly extending “Single Owner” schemes into “Multiple Owners” scenario still face the major two challenges: (1) the inconvenient key management and the resulting high communication cost; (2) due to the different authorities of owners, the qualities of documents are also different even if they are about the similar topic, but current rank functions in this area cannot rank documents based on their qualities. In this paper, we propose a secure multi-keyword ranked search scheme for multiple data owners. A trusted third party is imported to solve the problem of key management. We exploit the vector space model for generating index and query, and our new-designed KDO algorithm is utilized for providing keyword weight, so that the rank function not only considers about the relevance between query and document, but also takes into account the document quality. In order to protect privacy for both owners and users, the Asymmetric Scalar-product Preserving Encryption approach is utilized for encrypting weighted index and query. Besides, we construct the Grouped Balanced Binary tree index, which could further improve the search efficiency by Greedy Depth-first search algorithm. Extensive experiments demonstrate that our proposed scheme is secure, accurate and efficient.

In this study, a method was developed for diagnosing the non-thermal equilibrium physical properties of a free burning arc in air using the chromaticity information from a visible image. After extracting RGB values from the image and using digital image processing, the following two comparisons are made: (1) the red-component rate of arc for different electrode materials; (2) the chromaticity distribution at different stages of the AC arc; Analyses are also conducted based on the space radiation characteristics of the arc, differences due to the electrode material. Experiments showed that the proposed method can distinguish experimental materials and arc stages. Furthermore, since the image method is used, the method does not need to assume that the arc is in the thermal equilibrium stage.

In terms of optical image digital processing of gas discharge, the morphological research is mainly engaged in the image of UV, which has relatively few studies on discharge state by using grayscale texture information of visible images. Thanks to the progress of photoelectric detecting technique, the visible image contains more abundant space structure information, which makes it possible to detect the characteristic of grayscale texture information. In this paper, the 50Hz AC high voltage power source was applied to the 15mm needle-plane gap under room temperature and atmospheric pressure. The discharge images in the time scale of seconds were obtained by a high resolution digital camera. We introduce the research method of gray-level co-occurrence matrix into the corona discharge image processing. Contrast, entropy, correlation and the expected value ratio of vertical (90°) and horizontal (0°) directions are used to describe the state of corona development. The calculation results show that the contrast and entropy can effectively reflect the corona pre-breakdown state, and the correlation can effectively reflect the initial state. The expected value ratio can effectively reflect the process of growth, suppression, and gap-penetration of the discharge in the positive half cycle, which has a good correlation with the oscilloscope electrical signals. The method allows us to take advantage of visible light images to study the stages of AC corona, which provides a new method of low temperature plasma diagnostics.

With the rapid development of the Internet of things (IoT), more IoT devices are connected in the same network and communicate frequently for sharing data and switching instructions. So that the traditional centralized security architecture of IoT will be limited in terms of data storage space, data reliability, scalability and operating costs. In this paper, we propose a novel communication data management model based on blockchain technology. Specifically, all encrypted IoT communication data files are uploaded to public cloud server for obtaining enough storage space, but the key information extracted from these data files (called "communication logs") will be recorded in an "IoT ledger" maintained by all IoT devices. In fact, the IoT ledger is a kind of blockchain structured distributed database, which can solve the problem of data reliability, scalability and would not involve high operating costs. Besides, in order to efficiently search communication logs and not reveal any sensitive information of communication data, we design a secure search scheme over such blockchain database, the asymmetric scalar-product preserving encryption (ASPE) approach is exploited to guarantee the data security, and the two layers index improves the search efficiency. Security analysis and experiments on synthetic dataset show that our scheme is secure and efficient.

Discharge is a process, which involves changes in multiple species, and it is important for researchers to be able to describe changes in discharge status holistically. In this study, we applied AC and DC high voltage power sources to a short-gap needle-plane device in an atmospheric environment. We propose two new corona diagnosis methods based on spectral data and visible images. The experimental results demonstrate that, in the RGB system, the proportion of the blue component in AC corona images has the characteristic of decrease-stable-increase with voltage, and the corresponding result of the spectra is stable-increase. In the XYZ system, the AC corona chromaticity coordinates of both images and spectra demonstrate a linear fitting result with the voltage, and the image results contain a retracement process, showing shows the increase-stable-decrease trend of chromaticity coordinates x and y, which correlates well with oscilloscope results. The difference between the image and spectra results indicates that, compared to the spectra, high-resolution visible images are more sensitive to imperceptible changes in colorimetry information. The change of DC corona colorimetry characteristics can illustrate the change in AC corona colorimetry characteristics. Additionally, the dominant wavelength is proven to reflect the discharge species, and can reflect the vibrational temperatures. To the best of our knowledge, this paper represents the first attempt to systematically introduce colorimetry theory into discharge images and spectra. We successfully obtained colorimetric indicators with the linearity and low-dimension characteristics, which correspond well with known physical parameters.

Due to a variety of advantages of data outsourcing, some Location Based Services (LBS) providers are motivated to outsource the geographic data and query service to commercial cloud. However, for protecting data confidentiality, the valuable data should be encrypted before outsourcing, which obstructs the utilization like geographic information query. To address this problem, some previous works regarding to secure search on encrypted database could be applied in outsourced LBS scenario directly, but none of them is tailor-made for linear region search (LRS). The LRS is a kind of LBS that widely used in navigation system, it finds the nearby points of interest (POI) for a query segment. In this paper, for the first time, we explore and solve the challenging problem of privacy-preserving linear region search. Specifically, we choose the quadtree structure to build index for POI database, then the results of LRS can be efficiently obtained by finding out the rectangular regions that query segment passes through. In order to preserve the privacy of both LBS providers and users, according to computational geometry and Asymmetric Scalar-product Preserving Encryption (ASPE) approach, we design a novel algorithm for accurately determining whether a segment intersects with a rectangle on ciphertext. Moreover, this algorithm also provides a new idea to solve other computational problems in encrypted 2-dimensional geometry space. Based on different privacy requirements of two threat models, we propose two privacy-preserving LRS schemes and corresponding dynamic update operations. Security analysis and experiments on real-world dataset show that our schemes are secure and efficient.