Monitoring and controlling of process and quality characteristic for manufacturing system is a key issue in closed-loop quality control. Meanwhile, it is challenging due to the modern manufacturing system generally consists of hundreds of processes that are aligned to produce a specific end product. And the output quality of each individual process may be judged by dimensional quality characteristics. Each process and quality characteristic being monitored is costly and impractical. This paper attempts to provide a systematic approach to identify the key processes and quality characteristics simultaneously. Firstly, a modified casual matrix is used to acquire the correlation data between process and characteristic. The correlation degree is weighted by dynamic weighting function based on the importance of quality characteristics. Then, the triangular fuzzy function is used to construct the frame of discernment based on single index (quality characteristic). The mass functions that represent the degree of belief supported are determined and treated as pieces of evidence. Afterward, all of the evidence are combined by D-S (Dempster-Shafer) fusion rules. In addition, key quality characteristics are also identified based on the cumulative sum of the weighted score and Pareto Principle simultaneously. Finally, the usefulness of proposed approach is verified by a real-time dense medium coal preparation case.

To address the existing and limited original data and lower prediction robustness, a new multivariate regression forecasting model combined with gliding data barycenter was proposed. In this new forecasting method, the original data was interpolated and the corresponding data barycenter was optimized. Then, the important impact factors of ration consumption were analysed and chosen for the multivariate regression model. In simulation experiments, the training data of 35 years (1981-2015) were used, and the results have shown that the proposed method can greatly improve prediction accuracy and robustness.

Because there have been few studies on visual simulation of highline cable thus far, a simulation model that reflects how the shape of highline cable changes visually at various replenishment stages of alongside replenishment for solid cargo was proposed. With analysis of forces exerted on highline cable, expressions of several mechanical properties and parametric equations that described the shape of highline cable were deduced using the theory of catenary and structural mechanics, which comprised algorithmic sub models of different stages. Then, based on such algorithmic sub models, a series of physical sub models were established in the Unity platform. Both algorithmic sub models and physical sub models constituted the simulation model. This kind of model differed from the pure mathematical model and theoretical simulation model because it was able to not only display how the shape of highline cable changed qualitatively but also manifest values of these relevant physical properties in real time quantificationally. Results of executions indicate that this model can reliably mirror changes of highline cable at various stages of replenishment.

This paper focuses on finding, settling and reducing the program disturb error of 3D-TLC NAND flash memory. Experimental analysis of the FPGA test platform determines characteristics of the program disturb error. The program disturb error makes the state shift rate of storage cells lose balance. MSB, CSB and LSB pages have unbalanced bit error ratio and bit error rate distribution. The page program disturb bit error is unbalanced as the number of program/erase cycles changes. Based on the experimental results of the error rate imbalance, an error avoidance algorithm is designed, which can shift the data state that is subject to program disturb to one that is not vulnerable to program disturb. The test results show that the algorithm can reduce the error rate of program disturb by 20% to 90%. In this sense, the program disturb error phenomenon found and error avoidance algorithm designed in this paper are helpful for improving the reliability of 3D-TLC NAND flash memory.

Most researchers conduct defect detection under the assumption that the training and future test data must be in the same feature space and the same distribution. However, in the practical applications, data sets come from different domains and different distributions. Sometimes, local data in the target projects are limited and data are usually affected by noise. In these cases, the performance of the software defect detection model is uncertain. Firstly, we introduce the data complexity concept into the software engineering from data mining field. Secondly, we investigate the data complexity measurement on public software data sets to find out which complexity metric is appropriate to apply in defect detection. Finally, we analyze the relationship between complexity metrics and model performance to gain valuable insight into the effects of data complexity on defect detection. We are optimistic that our method can provide decision-making support for detection model management and design.

Reliability validation is an important research field in smart grid and involves the comprehensive consideration of various evaluation factors. Because it is difficult to accurately define the indicators of qualitative analysis, the fuzzy comprehensive evaluation method is introduced to evaluate each index and obtain reasonable, comprehensive and effective evaluation results. A novel model evaluation method called fuzzy analytic hierarchy process (FAHP) is proposed for smart grid in the area of energy internet, and it considers the interaction among the indices. The solution estimates the index weight through the analytic hierarchy process and builds the evaluation model using the fuzzy comprehensive evaluation method based on the hierarchy process to assess the performance of the evaluation model with fuzziness. Then, the effectiveness of the method for smart grid is verified by numerical examples. The fuzzy method proposed will provide an alternative technique for assessing different aspects of interaction in smart grid.

To alleviate 3D-HEVC computational burden, a novel depth level range determination method based on motion homogeneity is proposed in this paper. Since the multi-view videos show the same frame at the same time, it is important to use the selected depth levels and prediction modes to save coding time since they are highly multi-view content-dependent. The motion homogeneity model for CU is characterized by motion vectors and prediction modes with corresponding CUs in the reference views. According to this model, we propose a fast depth level range determination method. The experimental results show that the fast method is able to reduce the computation time by an average of 30.9%, while maintaining a similar rate-distortion (RD) value compared to the initial 3D-HEVC system.

Recommendation algorithms seek to predict user ratings or preferences. Due to limited information, it is difficult to make these predictions for new users. Therefore, a dynamic cold-start recommendation algorithm would be highly helpful in such quick-changing social networks. In this paper, a novel user group-based collaborative method, called UCFRA (User group-based Cold-start Friend Recommendation Algorithm) is proposed. UCFRA integrates a graphical model and statistical population characteristics into a user group model and then combines this extended user group model with cold-start information to generate a new recommendation algorithm. Moreover, a content popularity model based on user groups and a user rating matrix is designed. In order to improve recommend precision, a user group Top-N recommendation model based on k-nearest neighbors is provided. A series of experiments involving collection of a huge data set was developed to evaluate the effectiveness of UCFRA. The experimental results showed that UCFRA is a valid algorithm.

This paper reports on the development and verification of a novel formal symbolic process virtual machine (FSPVM) for verifying the reliability and security of Ethereum smart contracts, denoted as FSPVM-E, in Coq proof assistant. It adopts execution-verification isomorphism (EVI), an extension of Curry-Howard isomorphism (CHI), as its fundamental theoretical framework. The current version of FSPVM-E is constructed on a general, extensible, and reusable formal memory (GERM) framework, an extensible and universal formal intermediate programming language Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, and the corresponding formally verified interpreter of Lolisa, denoted as FEther. It supports the ERC20 standard and can automatically, simultaneously, and symbolically execute the smart contract programs of Ethereum and verify their reliability and security properties using Hoare logic in Coq. In addition, this work contributes to solving the problems of automation, inconsistency, and reusability in higher-order logic theorem proving.

In this paper, a novel electronic brake booster and an accurate pressure control method for engineering are put forward and consider the shortcomings of the existing braking system in active braking for intelligent driving. Together with the hydraulic control unit, they are comprised of two working modes: active braking for automatic drive and passive braking for driver intervention. For wheel cylinder pressure control, we translate the control problem of pressure to position tracking. The strong nonlinearity and the load-dependent friction make the position tracking control of the electro-mechanical brake booster more challenging. Consequently, a modified PI control architecture is presented with techniques of cascaded three closed loop PI controller, friction compensation based on friction model, and gain scheduling. Finally, based on dSPACE, we carried out the rapid prototyping tests of active braking and hardware in the loop tests covering full speed ACC conditions. The results show that pressure control, acceleration control, and speed control achieve optimal performance.

Traditional association rule mining algorithms usually generate association rules that satisfy the predefined minimum support and confidence threshold. However, with the rapid expansion of the data scale, the set of association rules obtained from traditional association rules contains massive redundant rules that cannot reflect the specific interests of users. In this paper, we combine the subjective interestingness optimization algorithm with the objective redundant association rule deletion algorithm in an effort to complement each of their shortcomings and improve optimization ability. We also present an algorithm for non-redundant association rules based on user interest orientation. First, an algorithm is proposed to remove redundant association rules based on the first-order predicate formula. Then, the non-redundant association rule set is used as the original data set for classification. By using the attributes or rules of the user's interest as the guiding object, the template idea is added as the carrier of the user's meaning. Based on the user’s prior knowledge, the template is divided into three types: GI, RPC, and PK. Based on this method, the association rules are obtained using the method of interest calculation. The experimental results show that the algorithm has been improved effectively.

With the development and popularization of Software-Defined Networking (SDN), security threats on SDN are on the rise. As one of the most common attacks on the Internet, Denial of Service (DoS) may possibly prevent SDN controllers from offering services through the consumption of network or system resources. This paper introduces Extenics into the research on strategy generation for the mitigation of DoS attacks against SDN controllers from the formal point of view. Considering the problem of generating the extension strategy for mitigation of DoS attacks against SDN controllers for SDN controllers, the extension strategy generation method based on Extenics is utilized. According to the performance indicators of SDN controllers, the generated mitigation strategy focuses on the dynamic adjustment of computing resources in order to reduce the negative effects of DoS attacks. The experimental analysis validates that the application of the extension strategy generation method is feasible and promotes the formalization to generate the strategy for mitigation of DoS attacks against SDN controllers.

Multimedia information, especially videos, is growing explosively with the rapid development of the Internet and multimedia technology. Due to its variety of image features, it is capable of reaching several hundred dimensions and even thousands of dimensions. Storing and indexing the high-dimensional feature vectors has become key technologies of content-based video retrieval. The residual quantization mechanism, which combines the asymmetric distance and set sorting algorithm based on multi-feature candidates, is improved after analyzing the characteristics of soccer videos. For soccer videos, SD-VLAD (Soft Distribution-Vectors of Locally Aggregated Descriptors), BOC (Bag of Color), and shot type are selected for describing the information of images. To address the problem that the original residual quantized inverted index can only retrieve single features, multiple feature retrieval and sorting are proposed. In the stage of candidate set sorting, a multi-feature based similarity calculation method is designed according to the shots type. The experimental results show that multi-feature hierarchical retrieval and sorting can be achieved at the cost of memory space. While ensuring query speed, the accuracy of the query is improved.

In this paper, a system of broadcasting football video conversion into 3D cartoon animation is designed. When a sports event is broadcasted, multiple cameras are usually deployed around the field. However, at the same time, only one camera’s video is available to viewers. Viewers hope to be able to watch the game from other viewpoints. Moreover, after a major sports game, some web portals provide cartoon animations of goal events. However, this is time-consuming and tedious, and only a single viewpoint is provided. Based on the proposed object tracking methods, this paper employs computer vision and computer graphics techniques to design a system that can generate 3D cartoon animations of soccer games. This allows users to watch the game from different viewpoints.

With the development of an intelligent microgrid transaction platform and blockchain technology, blockchain as a service (BaaS) on a cloud platform has received much attention. Currently, the problem of re-imaging the microgrid transaction mechanism is highlighted. Renewable energy must be consumed effectively and intelligently under the new transaction mechanism. To address these situations, this paper first builds the BaaS model, where blockchain technology is applied to the power transaction for intelligent distributed units. Then, a decision-making method is proposed for the intelligent unit to decide what it trades with. The method deals with transaction data in the cloud platform, obtains the probability distribution model of the microgrid transaction, and eventually achieves transaction decision making by using the Dempster Shafer (DS) evidence theory. The proposed transaction decision-making method provides more ways to reconstruct the microgrid transaction market and obtain effective management of market orders.

To address the trustworthiness of Internetware under open and dynamic environments, this paper proposes an approach to verify whether Internetware is satisfied with timing and resource constraints. Firstly, interface automata are extended with time and resource semantics. Then, timing and resource interface automata is used to model the behaviors of a software component. An algorithm is also provided to check whether all behaviors of an Internetware system are satisfied with resource constraints within a specified time. Finally, an online bookstore system is employed to illustrate our work, and the model checker Spin is used to verify the correctness of our approach.

Based on Hill matrix and dynamic DNA encoding, an image encryption method is proposed. First, this method combines an elliptic curve with a hyper-chaos sequence to fabricate the Hill cipher matrix, and the plaintext image is permuted and encrypted. Second, the dynamic DNA encoding technique is applied to diffuse the gray values of this plaintext image. Finally, hyper-chaos sequences are employed to complete the confusion and diffusion of the image. The experimental results show that the presented method can not only defend against brute-force attacks and statistical attacks but also resist plaintext attacks; in addition, the method has the merits of fast encryption speed, good encryption effects, and easy implementation.

Loss function is an important content in Bayes statistical inference. The task of this article is to study the reliability analysis of the exponential model based on a new proposed symmetric loss function. The new proposed loss function is established on the basis of the LINEX asymmetric loss function. Firstly, the Bayes estimation of the parameter is derived under the prior distribution of the parameter based on non-information Quasi prior distribution, and then the admissibility of the estimators are also discussed. Furthermore, this paper puts forward a novel testing procedure to evaluate the lifetime performance of exponential products based on the new derived Bayes estimator. Finally, Monte Carlo statistical simulation and an applicable example are used to illustrate that the new proposed Bayes estimators and testing procedure are effective and feasible.

With the ever growing amount of digital video data becoming available, people are gradually challenged to come up with methods that facilitate video indexing and retrieval. This paper presents a key frame based method that employs shot boundary detection and “bag-of-visual-words (BoW)” based on local keypoints for key frame extraction and semantic concept detection. The performance of BoW features is optimized by choosing appropriate representation choices. Once video frames are represented by BoW features, we can adopt a spectral clustering algorithm for the generation of key frames in each shot, and then we can classify these key frames using support vector machines for video indexing. Finally, this paper performs a query by concept search for video retrieval. The experimental results demonstrate that the proposed approach is capable of retrieving videos. Compared with the existing related method, the proposed method yields better results for key frame extraction and yields a mean average precision (MAP) of 0.68 for the video retrieval model.

This study analyzed the operational characteristics of the reliability and maintainability of the Projection Spot Welding (PSW) machine in the automotive industry. The components of the machine were grouped into three sub-systems: electrical, pneumatic, and hydraulic, and they were ordered into a hierarchical structural model for calculating reliability characteristics. The obtained maintenance lists and failure and repair data were studied and classified. Trend and serial correlation tests were carried out to select a proper modeling technique for each sub-system. Then, the reliability-maintainability model was constructed to estimate the failure behavior of the sub-systems, identify the best-fit distribution model, and calculate reliability characteristics. Finally, based on the calculated reliability characteristics, the preventive maintenance intervals for a different reliability level were calculated and requirements for increasing reliability were suggested. The analysis of the main structural sub-systems of the RSW machine showed that the hydraulic sub-system significantly affects the overall reliability level of the machine. However, the mean time to repair (MTTR) of the hydraulic sub-system is the lowest among other sub-systems. The analysis also found that the reliability of the electric and pneumatic sub-systems decreases by up to 50% after approximately 80h and 40h, respectively. The results of the study concluded that the reliability and maintainability analysis can improve the accuracy of preventive maintenance intervals and assess the reliability of each component of the RSW machine, which in turn can help reduce operating costs and extend the life of machine components.

3D extension of high efficiency video coding (3D-HEVC) has become a video compression international standard for multi-view video system applications. The mode prediction process must search for optimal prediction modes and depth levels with a minimum rate-distortion cost for the coding unit (CU). New coding techniques with improved texture video coding efficiency have been introduced; however, they also increase computational burden, which obstructs 3D-HEVC encoder reliability applications. Therefore, the development of a novel texture video coding is necessary to reduce 3D-HEVC computational complexity. In this paper, the proposed algorithm will exploit an adjusted method of DE and ME based on motion homogeneity of texture videos. The algorithm includes early SKIP/Merge mode decision and adaptive motion search range adjustment. As shown by the simulation results, the proposed fast texture video coding method can reduce computational complexity by approximately 56.6% on average while resulting in a tiny loss of quality.

The proposal of the multi-controller has improved the scalability and reliability of software-defined networking (SDN), and the entire network is divided into several subdomains with the self-governed controller. Due to the dynamic changes of network traffic in different subdomains, a new challenge has emerged for balancing loads on the distributed controllers. Switch migration, a promising method, is designed to solve the unbalanced distribution of controller loads. However, existing schemes implement switch migration without considering migration sequences and are characterized by long migration time and poor controller processing efficiency. To solve the above problems, this paper proposes the Sequenced Switch Migration (SSM) approach, which considers migration parameters (e.g., delay, traffic, residual capacity, and failure probability) for multi-objective optimization and achieves efficient switch migration using a sequenced manner. By executing the parallel and heuristic algorithms, SSM can reduce the execution time of migration while ensuring the performance requirements of controllers. The simulation results show that compared with the existing schemes, SSM reduces the total migration time by about 32.7% and decreases controller load variance by 28.5% when controllers are under high load condition.

To realize the convenient and intelligent monitoring and management of cable manufacturers’ production lines, this paper designs a set of meter management systems based on the Internet of things that can replace artificial monitoring cable production. The system uses the STM32 processor of the ARM kernel as its core, and it combines embedded technology, wireless communication technology, Internet of things technology, 485 bus technology, and upper computer software technology. The system includes the front-end signal processing equipment, the Internet of things server, and the client software, which can respectively realize the information collection and automatic control of the cable production line, real-time transmission of production data under the MQTT protocol, and storage and graphical display of production data. The system was tested in the factory and proved the timeliness of the control of the production line, the real-time nature of the data transmission, and the practicality of the client software, which plays a great role in the modernization of the traditional factory.

To facilitate the data acquisition and analysis of hydrogen sensors in early stages, a set of data acquisition and processing systems for hydrogen sensors has been developed. The system applies the embedded technology and combines the acquisition terminal with the upper computer to realize the data acquisition and processing of hydrogen sensors. At present, it has been applied to a constant voltage type hydrogen sensor to collect and process data. The conversion from resistance to current to voltage is realized. The resistance value at both ends of the measured material is within 200 KΩ. The measurement accuracy is 1 Ω. The upper computer assists with the temperature compensation mechanism to achieve the effective data acquisition and processing of the measured materials. The practical application shows that the system is of great significance to the data acquisition and processing of early hydrogen sensors.

With the continuous optimization and reform of the healthcare system, many of the equipment in hospitals are developing in the direction of intelligence. At present, the hospital bed call system relies mainly on the traditional cable. Wired call system wiring is tedious and high in cost. It can sometimes cause short circuits, circuit breakages, and other problems, which will affect the timely treatment of patients. Based on ZigBee CC2530 software and hardware technology, this paper designs a wearable wireless calling system in hospitals based on ZigBee. ZigBee technology is a short distance wireless communication technology, which is characterized by low power consumption and low cost. This article sets up the style of the watch based on the ZigBee wireless communication hardware. The nurse can detect the patient's location at all times through the wireless call system device worn by the patient. When a patient is away from his or her hospital bed and suddenly needs care, the patient can call the nursing station according to the wireless communication system worn on his wrist. The nurse can arrive quickly and carry out the treatment, which can greatly alleviate any emergencies. The application of this wireless call system has significantly improved the level of hospital care and further promoted the development of hospital equipment in the direction of intelligence.

In recent years, cloud computing has been wildly used to improve the quality of mobile services in wireless networks. The ability of cloud computing gives the new mobile cloud system (MCS) the power to provide high-performance services. However, these services may be interrupted by random resource failures, such as network failures and virtual machine (VM) failures, which extremely affect the service performance in real-life scenarios. In this paper, we present a theoretical modeling approach to evaluate the service performance of the MCS, which takes into consideration the impact of reliability on service performance. The queue theory and the Markov chain are first used to model the performance and the reliability. In reliability modeling, the network failure and VM failure are considered. Then, a Bayesian method is applied to connect the performance and the reliability, and a correlation metric is proposed. Finally, numerical examples are illustrated.

As logging code evolves with bug fixes and feature updates, developers may miss some log revisions due to a lack of general specifications and attention from developers. This makes it more troublesome to achieve good logging practices. In this paper, we try to study log revision behaviors from evolutionary history. Motivated by similar edits of clone codes, we assume there also exist similar log revisions that implicated log revision behaviors. Based on this assumption, we study the similarity of log revision behaviors and answer six research questions. Specifically, we find that 54.14% of log revisions belong to groups of similar log revisions and 64.4% of groups contain log revisions that are missed by developers. We stress the importance of branch statements on learning from similar log revisions since 53.51% of sampled similar log revisions are related to the semantics of branch statements.

The progress of human society cannot be separated from medical development. CT is an important auxiliary instrument of modern medicine, and its imaging precision and stability are essential conditions. To provide a more accurate reference for medicine, data fitting, parameter estimation, interpolation, neural network, continuous discretization, and image processing algorithms are applied in this paper to establish a corresponding model between absorption strength and images through Radon transform and Iradon transform, thereby demonstrating the CT imaging process and CT parameter calibration process. Meanwhile, the CT imaging rules are obtained. As a result, CT parameter calibration precision and stability are improved, as well as diagnosis precision. First, the Excel form is compressed according to the data in Annex 1, and the characteristics of data in Annex 2 are observed. Horizontal illumination and vertical illumination of light are taken to determine the detector unit space. When horizontal light illuminates the medium, the chord passing through the center of the circle is longest. The energy magnitude, physical relation between energy and thickness, and length of the corresponding chord can be found in Annex 2 to establish the matrix equation set and solve the center of rotation of the CT system. The data in Annex 2 corresponds to 180 directions, and the equation set is established to gain every direction.

In this paper, we put forward a novel optimization framework entitled the E-commerce Online Auction Machine. Considering all the characteristics that affect online auction prices, the algorithms are applied to calculate the best fitting line to predict online auction prices by ordinary least squares. After that, regression weights are optimized using the local weighted method. Finally, using the shrinkage method, each characteristic optimal weight is obtained through the EOAM-RR algorithm. We have identified the key characteristics that affect auction prices as well as those that are not important.

In order to satisfy transmission and application, this paper illustrates the drawback of the current rate-distortion model and the characteristics of multi-view video coding (MVC). This paper proposes a bit allocation and rate control scheme for MVC based on frame complexity and human visual characteristics. Firstly, the proposed algorithm improves the quadratic rate-distortion (R-D) model. Then, the proposed algorithm reasonably allocates bit-rate among views based on frame complexity and human visual characteristics. This paper turns the bit allocation among views into a multi-objective optimization problem. Finally, this paper proposes a view layer and frame layer bit allocation algorithm for MVC according to frame complexity and the previously coded information. Simulation results show that the proposed algorithm can effectively control bit rate for MVC, where the average rate control error of our algorithm is 1.08%.

Volume rendering is an important technique in data visualization, but it has some problems, such as low sampling efficiency and slow drawing speed. This is especially true for the model of body visualization calculation, where the scene rendering speed is too large and the throughput rate is too high. To improve the drawing speed of the volume rendering algorithm, a method of parallel GPU computing is proposed to optimize the algorithm. A parallel volume rendering algorithm based on GPU is implemented to improve the rendering speed, and the experimental results show that the algorithm can improve the speed without affecting the image quality.

Compressed sensing (CS) is a novel and important technique in MRI reconstruction, which can be used to reconstruct magnetic resonance images with few sampled data while simultaneously speeding up imaging. The conventional CS-based MRI is implemented from a global view, which leads to some disadvantages: it not only loses many local structures but also fails to preserve detail information. To obtain better reconstruction quality, we propose a novel CS-based reconstruction model, which is incorporated with nonlocal technology to allow for the preservation of extra details. The proposed model groups similar patches within the nonlocal area and stacks them to form a 3D array unit. Then, to process the array in a realistic 3D manner, a tensor-based sparsity constraint is developed as the regularization on the reconstructed image. Experimental results show that the performance of the proposed method is superior to those of conventional methods.