k-way graph partitioning is an NP-complete problem, which is applied to various tasks such as route planning, image segmentation, community detection, and high-performance computing. The approximate methods constitute a useful solution for these types of problems. Thus, many research studies have focused on developing meta-heuristic algorithms to tackle the graph partitioning problem. Local search is one of the earliest methods that has been applied efficiently to this type of problem. Recent studies have explored various types of local search methods and have improved them such that they can be used with the partitioning process. Moreover, local search methods are widely integrated with population-based approaches, to provide the best diversification and intensification for the problem space. This study emphasizes the local search approaches, as well as their combination with other graph partitioning approaches. At present, none of the surveys in the literature has focused on this class of state of the art approaches in much detail. In this study, the vital parts of these approaches including neighborhood structure, acceptance criterion, and the ways of combining them with other approaches, are highlighted. Additionally, we provide an experimental comparison that shows the variance in the performance of the reviewed methods. Hence, this study clarifies these methods to show their advantages and limitations for the targeted problem, and thus can aid in the direction of research flow towards the area of graph partitioning.
A new method of vital stimulation apart from the visual and auditory senses using modulated far-infrared rays is proposed. The somatic sense is stimulated to provide the feeling of presence of a human being. Fundamental experiments are performed, and the specifications of the stimulating device are defined. The heartbeat, breathing, and temperature change of parts of the face are observed during the irradiation of the modulated far-infrared rays. The change in the vital data of the human reaction towards the stimulation is < 3.5% and no remarkable characteristics are observed. When three subjects are questioned, a somatic sensation is reported; thus, the possibility of detecting the feeling of presence using other vital data is suggested. Further development of detection techniques using the vital data is planned in order to confirm the effectiveness of the detection of the feeling of presence.
A recommender system is an important tool to help users obtain content and overcome information overload. It can predict users’ interests and offer recommendations by analyzing their history behaviors. However, traditional recommender systems focus primarily on static user behavior analysis. Recently, with the promotion of the Netflix recommendation prize and the open dataset with location and time information, many researchers have focused on the dynamic characteristics of the recommender system (including the changes in the dynamic model of user interest), and begun to offer recommendations based on these dynamic features. Intuitively, these dynamic user features provide us with an effective method to learn user interests deeply. Based on the observations above, we present a dynamic fusion model by integrating geographical location, user preferences, and the time factor based on the Gibbs sampling process to provide better recommendations. To evaluate the performance of our proposed method, we conducted experiments on real-world datasets. The experimental results indicate that our proposed dynamic recommender system with fused time and location factors not only performs well in traditional scenarios, but also in sparsity situations where users appear at the first time.
Lake Sakurako is a reservoir of the Miharu Dam in Fukushima Prefecture, Japan. The water quality of the small lake becomes significantly worse during the summer owing to the occurrence of blue-green algae. Therefore, water quality management is a serious problem. Because the primary method of water quality analysis is direct collection from the target water area, the analysis range is limited, and the analysis of the entire water area is very difficult. Therefore, performing a wider range of analyses by remote sensing is a possible solution. In this study, we analyze near infrared (NIR) data acquired by unmanned aerial vehicles (UAVs). A fuzzy regression analysis is conducted on the UAV data and water measurements. Based on the experimental results of data from August 2015, the NIR data is confirmed to be useful in estimating the water quality conditions in Lake Sakurako. Furthermore, we investigate the noise removal process using a nonlocal mean filter and demonstrate that the process provides more detailed information regarding the lake’s water quality.
The faults in through-silicon via (TSV) have a critical impact on the reliability and yield of a three-dimensional integrated circuit (3-D IC). With the significant increase in the number of TSVs used in 3-D IC, the testing of TSVs for manufacturing faults poses certain serious challenges especially weak fault testing, and therefore it is important to have effective Design-For-Test (DFT) techniques. In this paper, we present a method for TSV testing using multi-tone dither signal, based on electrical characteristic analysis. This method mainly observes the differences in the root mean square (RMS) value of the output signal voltage between faultless and faulty TSV circuits to detect manufacturing faults, and uses only passive components such as metal lines, without consuming additional power for the testing. With regard to the common manufacturing faults such as voids and pinholes, the electrical characteristics of faulty TSVs are modeled and analyzed, and analytic equations of the faults, which are based on characteristic parameters, are explored. The ground-signal-TSV (GS-TSV) equivalent electrical model with manufacturing faults is simulated and tested by using a multi-tone dither test signal, which is generated by modulating an RF signal with an optimized multi-tone signal. The peak-to-average ratio (PAR) is used as the test evaluation parameter to determine the type and size of the fault. The simulation results demonstrate the effectiveness of the multi-tone dither test method in the detection of voids (as low as ohm level) and pinholes (up to mega ohm level). It is obvious that this method performs better in the diagnosis of weak manufacturing faults in TSVs.
To respond to changes in the requirements of the information and control system, it is necessary to rebuild the system with new functions and/or systems with a different architecture. In this paper, we propose a method that enables on-site technicians to perform step-wise migration from a legacy system to a target system without having to stop the whole system and without requiring advance preparation or support from a system engineer who is an expert in system design and development. Accordingly, we aimed to develop a technology for the coexistence of and cooperation between with target and legacy systems and cooperation test between heterogeneous systems. We implemented this method and evaluated its effectiveness by applying it for rebuilding a production line management system.
In this paper, we propose an analog value associative memory using Restricted Boltzmann Machine (AVAM). Research on treating knowledge is becoming more and more important such as in natural language processing and computer vision fields. Associative memory plays an important role to store knowledge. First, we obtain distributed representation of words with analog values using word2vec. Then the obtained distributed representation is learned in the proposed AVAM. In the evaluation experiments, we found simple but very important phenomenon in word2vec method: almost all of the values in the generated vectors are small values. By applying traditional normalization method for each word vector, the performance of the proposed AVAM is largely improved. Detailed experimental evaluations are carried out to show superior performance of the proposed AVAM.
In order to ensure the stability and economy of ultra-high voltage grid in construction, we need to research the intelligent control method of ultra-high voltage grid. Using current method in ultra-high voltage grid construction, there is a problem of poor stability. Therefore, this paper proposed an intelligent control method of ultra-high voltage grid. This method analyzed the transmission capacity of power grid and electromagnetic loop operation, and used the genetic algorithm to compute the optimization model, finally analyzed the stability of the power frequency voltage completing the intelligent control of ultra-high voltage grid. Experimental results show that this method has high practical value.
With the rapid development of mobile internet and smart city, video surveillance is popular in areas such as transportation, schools, homes, and shopping malls. It is important subject to manage the massive videos quickly and accurately. This paper tries to use Hadoop cloud platform for massive video data storage, transcoding and retrieval. The key technologies of cloud computing and Hadoop are introduced firstly in the paper. Then, we analyze the functions of video management platform, such as user management, videos storage, videos transcoding, and videos retrieval. According to the basic functions and cloud computing, each module design process and figure are provided in the paper. The massive videos management system based on cloud platform will be better than the traditional videos management system in the aspects of storage capacity, transcoding performance and retrieval speed.
The traditional method cannot make predictive judgment on the future load of the system, which leads to the convergence speed in the local updating process and cause the waste of resources. Aiming at this problem, a data equalization method based on ant colony optimization algorithm is proposed. During the calculation of server cluster integrated load, two kinds of load information input indicators and server indexes are mainly used. A formal description of the task scheduling problem under the high load of distributed parallel database is carried out and the mathematical model is established; the independent and different resource required virtual machine in the system are deployed in the server to balance the system, which has good global convergence, and can effectively control the system resource usage. Experiments showed that the proposed method avoids the unwanted migration caused by the instantaneous peak, which reduces the overhead of the system.
In order to solve the high peak to average power ratio (PAPR) problem of pseudo random code phase modulation (PRCPM) signals, minimum shift keying (MSK) modulation waveforms with constant envelope were introduced into underwater detection. Genetic algorithm (GA) was proposed to optimize pseudo random binary codes used for MSK waveforms, in order to design sonar waveforms with various performances. After MSK complex envelope signal was obtained by theoretical analysis, the optimizing objective functions for a single waveform and a group of waveforms were presented. The optimized single waveform with low autocorrelation sidelobe values can reduce false alarm number and the difficulty of target decision. When multiple sonar systems work as a team, the optimized group of orthogonal waveforms with low autocorrelation sidelobe values and cross-correlation values can alleviate interferences between each other. In the simulation, the correlation performances of a single waveform and a group of orthogonal waveforms were presented, and ambiguity function showed that the designed waveforms had good velocity and distance resolution, which means that the optimized MSK waveforms are suitable for underwater detection.
This study explores the impact of the usability of E-commerce websites on user satisfaction and provides a reference for designing and assessing E-commerce websites, with the aim of improving websites usability for enterprises. This study proposes research hypotheses on the impact of the usability of E-commerce websites on user satisfaction based on Microsoft’s usability guidelines. In addition, the weighted scores and correlation analysis are used to test the hypotheses. One hundred and twenty three participants were selected for filling out questionnaires. Some countermeasures to improve E-commerce websites from the perspective of website usability are proposed in the end of this paper. The usability of web content, personalized services, and emotional states have the greatest impact on users’ satisfaction, which provides the relevant theoretical guides on usability construction of E-commerce websites.
For the defect of the traditional vanishing point detection algorithm that is invalid in unstructured environment, a novel vanishing detection algorithm based on Dynamic Template Matching (DTM) is proposed. And a framework of access area recognition is put forward according to the vanishing point line. First, a series of lines are selected from the image in the form of the scanning at the same interval and then calculate the between each line and the previous one. The horizontal position of vanish point is that of the line with the minimum normalized correlation value in all scanning line. Second, a new image is constructed by getting rid of the part above of the viewpoint line, and be divided into several subimages without overlap to extract the multi features. The end, a train set is constructed based on the assumption of no deviation of the vehicle and the test set is classified by multi-kernel learning (MKL) method to obtain passable area. In addition, according to the need of intelligent vehicles during working, a weight-accuracy is delimited by assigning the different weights to the near areas and far areas. This kind of accuracy is more significative than the original one. In the experiments on various environments image sets, the proposed method exhibits favorable performances compared to the other methods.
The transformation of new and old kinetic energy is a new requirement for the new economy when China enters into the new era. When mankind experienced mechanization, electrification, automation, and finally entered the era of digital industry, human wisdom based on information technology will become a new energy source for the new era. The trend of Digital, networked, automated and intelligent economic development has become the main driving force of energy innovation under the background of big data, while smart city construction, as a kind of infrastructure investment, assumes the function of upstream industry and social leading capital in the conversion of new and old kinetic energy. The experience of leading the construction of smart city of Weifang based on the NB-IoT unified standards, sequential upgrading development, and people’s livelihood guidance has further proved that smart city construction is not only the specific application of big data in infrastructure construction, but a high point of development in the new round of digital economy and new and old energy conversion transformation.
To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.
To promote the road transportation security, it’s necessary to study the modeling method of driving behavior characteristics. The traffic flow model realized by current modeling methods of driving behavior characteristics has a low accuracy in warning results. Therefore, based on satellite positioning data, a modeling method of driving behavior characteristics is proposed in this paper. Firstly, the dynamic model and kinetic model of traffic flow are built through the flow, speed and density parameters; then the response time, minimum safe distance and stability parameters of driving behavior are taken as the identification index of driving behavior to identify the driving behavior of drivers; according to the identification results, the psychological field theory and satellite positioning data are combined to build the model of driving behavior characteristics, and finally, warning the drivers according to their psychology and the actual situation of road. Experimental results demonstrate that the proposed method can accurately measure the traffic flow and speed, and the score of drivers’ behavior obtained has high accuracy, which verified again the high accuracy of traffic flow model and warning results of the proposed method.
The current image encryption method is relatively simple, and there is the problem of poor image encryption effect. Based on the hybrid chaotic model, an image encryption method with component fusion is proposed in this paper. The image is mapped by using Arnold cat mapping method. Chaotic sequence is generated by chaotic model, and the original image is scrambled and substituted to achieve image encryption. Through the fixed point ratio, information entropy, gray mean change value, autocorrelation and similarity, the test of encrypted image is completed. Experimental results show that the proposed image encryption method has good performance, high security intensity, and can effectively encrypt the image.
The current communication scheduling algorithm for smart home cannot realize low latency in scheduling effect with unreasonable control of communication throughput and large energy consumption. In this paper, a communication scheduling algorithm for smart home in Internet of Things under cloud computing based on particle swarm is proposed. According to the fact that the transmission bandwidth of any data flow is limited by the bandwidth of network card of sending end and receiving end, the bandwidth limits of network card of smart home communication server are used to predict the maximum practicable bandwidth of data flow. Firstly, the initial value of communication scheduling objective function of smart home and particle swarm is set, and the objective function is taken as the fitness function of particle. Then the current optimal solution of objective function is calculated through predicted value and objective function, current position and flight speed of particle should be updated until the iteration conditions are met. Finally, the optimal solution is output, the communication scheduling of smart home is thus realized. Experiments show that this algorithm can realize low latency with small energy consumption, and the throughput is relatively reasonable.
At present, storage technology cannot save data completely. Therefore, in such a big data environment, data mining technology needs to be optimized for intelligent data. Firstly, in the face of massive intelligent data, the potential relationship between data items in the database is firstly described by association rules. The data items are measured by support degree and confidence level, and the data set with minimum support is found. At the same time, strong association rules are obtained according to the given confidence level of users. Secondly, in order to effectively improve the scanning speed of data items, an optimized association data mining technology based on hash technology and optimized transaction compression technology is proposed. A hash function is used to count the item set in the set of waiting options, and the count is less than its support, then the pruning is done, and then the object compression technique is used to delete the item and the transaction which is unrelated to the item set, so as to improve the processing efficiency of the association rules. Experiments show that the optimized data mining technology can significantly improve the efficiency of obtaining valuable intelligent data.
The location algorithm based on arrival time difference has high complexity and high system power consumption. When using the traditional TDOA positioning algorithm to locate, we will encounter over-determined nonlinearity of positioning equation, and you need to use iterative method to solve it. Based on this, this paper proposed a hybrid positioning algorithm of TDOA and AOA. Combining the TDOA and AOA information, the problem of solving the iterative positioning equation was simplified successfully, which reduced the complexity of the algorithm and reduced the system power consumption. At the same time, in order to solve the problem of high system power consumption, this paper also proposed the use of Zigbee protocol instead of the traditional Wi-Fi protocol to develop the positioning system, thereby effectively reducing system power consumption, increasing system life and improving system applicability.
The sports video analysis system in sports training can improve the ability of motion analysis and improve the training quality of sports training video playback. In view of the shortcomings of the current motion video analysis system, a new sports training video analysis system is proposed. The overall design of the video analysis system of the sports training system is analyzed, and the detailed design of the system is analyzed. Finally, the performance of the system is tested. The results show that the system can accurately analyze the video and image information of sports training. The accuracy of the key frame extraction is high and the recall rate is high. It can be used to guide the training of sports training.
Although the present attendance management system, adopted by universities, determines students’ physical presence. It does not determine whether they perform physical activities. It is important to monitor students’ extracurricular physical exercise scientifically and effectively to solve the actual effect of extracurricular physical exercise attendance and exercise. Calorie management is one solution to this problem. Additionally, an extracurricular physical exercise monitoring and management system is developed to record the energy consumption of students during their physical activities. To realize the demand for the management of calories and the monitoring and analysis of the energy consumption of students through the two development of the energy consumption instrument. This plan has certain significance for solving the actual effect of extracurricular physical training.
The data recorded by current algorithms contains more errors, which reduces the quality of hyperspectral remote sensing images and affects the fusion results. A fusion algorithm based on improved IHS transform is proposed. In order to avoid the noise and diffusion spread and the uniform distribution of gray level, the detail information is preserved and the image is geometric corrected, denoised and histogram equalized. Then the feature extraction, edge detection and feature matching are performed to the images. The weighted average fusion criterion is used to improve the fusion algorithm of IHS transform to improve the spectral distortion of fusion images. Through statistical and visual interpretation of evaluation results, the proposed fusion algorithm preserves the original spectral information and has good visual effects, which is more in line with human subjective evaluation criteria.