Time-series data is the key to industrial development, with the aggregation of the data an important step in practice. However, traditional relational databases fail to support vast amounts of time-series data. The NoSQL databases are inefficient and require time-consuming calculation to aggregate of time-series data. This paper presents an efficient index mechanism that supports time-series data aggregation by combining a synopsis table and a segment tree. A query algorithm based on this mechanism introduces the synopsis table into the NoSQL database and builds a segment tree from the synopsis table for archiving that is lbn the size of the original query set. This query algorithm can directly locate a series of index data to be queried without the recursive operations in traditional trees and effectively reduces I/O overhead. This study shows the efficiency of this index mechanism by comparisons with general index mechanisms.

With the increasing complexity of the large data processing systems, Byzantine fault behavior caused by software bugs, misconfiguration or malicious intrusions can seriously affect system availability. Byzantine fault nodes can then cheat other correct nodes by equivocation behavior, so the equivocation behaviors can be detected to maintain system safety. This paper presents a security log with integrity verification support, Log-Keeper, which generates integrity proofs for existence and consistency verification. Frequent verification in a distributed environment uses an IndexTree-based Log-Keeper which supports O(lbn) proof. Tests show that the size of the integrity proofs generated by Log-Keeper is 25%~50% of that of the authenticated append-only skip list (AASL).

More and more hardware platforms are providing dynamic partial reconfiguration; thus, traditional hardware/software partitioning algorithms are no longer applicable. Some studies have analyzed the dynamic partial reconfiguration as mixed-integer linear programming (MILP) models to get solutions. However, the MILP models are slow and can only handle small problems. This paper uses heuristic algorithms to determine the status of some critical tasks to reduce the scale of the MILP problem for large problems. Tests show that this method is about 200 times faster with the same solution quality as the traditional mathematical programming method.

The search space for discovering constant conditional functional dependencies (CCFDs) is reduced and the efficiency is improved by a series of pruning strategies that optimize the algorithm CFDMiner, which is a popular algorithm for mining CCFDs. Theoretical studies show many invalid and redundant free and closed itemsets for outputting valid CCFDs. Thus, pruning of free itemsets and selecting of corresponding closed itemsets can generate as consistent results as the original algorithm. Tests show that the optimized algorithm has a smaller search space and its efficiency is improved 4~5 fold on true data.

Unbalanced distributions of traffic in the data center networks increase the network congestion. The special traffic characteristics in data center networks reduce the effectiveness of traditional IP network traffic engineering methods. This paper presents a dynamic routing algorithm based on multipath propagation (Dramp) using software defined for network/OpenFlow (SDN/OF) frameworks. This algorithm makes efficient use of multiple paths by redefining the degree of the critical link and solving an optimization problem using link weights. The objective is to reduce the overhead in the control plane for a fine-grained traffic balance for routing optimization in data center networks. This paper compares the Dramp algorithm with the equal-cost multipath (ECMP) and global first fit (GFF) routing algorithms on the Mininet simulation platform. The results show that Dramp gives the best performance; thus, Dramp is simpler and more practical for traffic engineering in data center networks.

The need for copyright protection and the desire to protect intellectual property has made content based video copy detection a widely studied issue. A video copy detection method based on local and global features is given here that overcomes the limitations of using the global and local features. The system combines blocked Harris corner and ordinal measures and is robust to illumination changes, video coding changes, partial occlusions and other effects. The system also uses inverted indexes, so this method is very efficient. Tests show that this algorithm effectively improves the accuracy of the video copy detection.

This paper presents an algorithm named OCBA_OODE for on-road trajectory planning by using optimal computing budget allocation (OCBA) in a candidate-curve-based planning algorithm named OODE. OODE picks the best trajectory by comparing rough (biased but computationally inexpensive) evaluations of a set of candidate curves. The curve evaluation converges to the real value as the computing budget increases. OODE allocates the equal parts of the computing budget to each curve, while OCBA_OODE repeatedly allocates the budget according to the latest curve evaluations to improve the planning efficiency. OCBA_OODE is 20% faster than OODE while maintaining the same solution quality.

A robust framework is given for precise vehicle localization in intersections using distributed video networks. Each intersection is equipped with short-range and long-range cameras in a distributed video network. If the vehicle is in the shooting range of the short-range camera, within the region of interest for vehicle identification, and the license plate is perpendicular to the road plane, a vehicle license plate model is used to accurately locate the vehicle position. If the vehicle is in the shooting range of the long-range camera, a pyramid sparse optical flow algorithm with LBP texture features is used in real-time to track the local feature points on the vehicle to estimate the vehicle position based on stable feature points obtained from the similar motions. Finally, information is exchanged between the cameras, a weighted consensus information fusion algorithm is used to obtain a globally optimal estimate of the vehicle position. Tests show that this method can accurately locate the vehicle position at intersections.

In recent years, an increasing amount of traffic information has been posted on social media such as micro-blogs. This information provides a new opportunity for traffic analysis using micro-blog traffic data to supplement traditional traffic data. The micro-blog data has been analyzed to identify frequently-used natural language description of traffic conditions with fuzzy assessments used to quantify the subjective feelings of different people describing traffic congestion with natural language. The fuzzy reasoning data fusion method aggregated evaluations by different people describing the congestion of the same section of a road. Videos were collected from three road segments with observers invited to evaluate the road traffic conditions in the videos. The integration results are similar to the real-time traffic scenarios released by Baidu Map, which verify the feasibility of this fuzzy method.

The plastic encapsulated microcircuits (PEM) burn-in method helps guarantee the reliability of PEM. Thermal runaway and uneven temperatures between different devices during burn-in are controlled by a closed-loop temperature control system. The control system adjusts the temperature of each burn-in socket relative to the oven temperature using a fuzzy adaptive algorithm to configure the control parameters of the PI controller that drives the heating or cooling actuators. The device temperature more quickly achieves steady state (20 min) with steady state errors of less than 1℃ and the temperature differences between the tested devices are reduced by 2℃ so that the temperature are almost the same with the closed-loop temperature control system. The fuzzy adaptive algorithm makes the system dynamic response much faster with stronger anti-interference ability. Thus, this system prevents thermal runaway and makes the temperatures equal between the different tested devices.

High-speed, narrowband power line communications (HNPLC) need effective application layers to control the power system demand response to support various services. This study defines an application architecture for an interactive HNPLC system to support automated demand response. The multi-class service demand is provided by a dynamic priority that considers the data packet priority weights, packet transmission efficiency and service waiting times. The service response mechanism, the core of the application layer, is based on the dynamic priority. The application layer is within the cross-layer HNPLC nodes in a simulated practical HNPLC network. Simulations show that the architecture is efficient and the service response mechanism improves the HNPLC performance for the automated demand response.

A nonlinear digital PID single loop control method was developed to improve the dynamic load response of the front end power supply of magnetic imaging resonance (MRI) gradient amplifiers with the controller parameters are based on the exponential functions of the error. The dynamic load response is improved by an inner-loop current controller added to the nonlinear digital PID controller. The two control methods are verified in a 120 V/30 A prototype of the power supply for a step load current change and compared with the dynamic physical limits of the system. The results indicate that the controller has better dynamic performance which is closer to the dynamic physical limits of the system that the original nonlinear PID controller.

The adsorption of uranium from wastewater onto activated carbon was investigated in batch experiments. Four independent variables, the contact time, solution pH_a, initial uranium concentration and temperature, were varied to determine the influence of these parameters on the adsorption of the uranium from water. The activated carbon size were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), with the element content characterized by energy dispersive X-ray spectrometer (EDS) and surface functional groups characterized by infrared spectrocopy (FTIR). The FTIR spectra indicated that hydroxyl groups are present on the surface of the activated carbon and affected the adsorption. The U(Ⅵ) adsorption onto activated carbon reached sorption equilibrium within 30 min. The adsorption of U(Ⅵ) on activated carbon was strongly dependent on the pH_a in the range of 3.0~9.0. The adsorption capacity and removal percent increase for pH_a from 3 to 5, decrease for pH_a=5~7, and then increase quickly for pH_a=7~9. The U(Ⅵ) adsorption on activated carbon is well described by a pseudo-second-order kinetic model and the Tempkin, Slip, and D-R isotherm models. The sorption reaction is spontaneous, exothermic and increases the entropy. The maxmium adsorption capacity is 62.50 mg·g^-1. The maxmium removal rate is 99.23%.

A failure propagation analysis method was developed for safety systems having multiple interactive sub-systems. Two models are given based on min-plus and max-plus algebra to describe the failure propagation and control processes. Iterative solutions for both models give the final hazardous output disclosure time for specific failures. Unlike other failure propagation models, this method describes the asymmetry in the failure propagation mechanism and presents a calculational method for the hazardous incident time for interactive safety systems. This method is applied to a conceptual CTCS system to demonstrate its effectiveness.

Magnetic compasses are widely used in civil and military navigation applications but they are susceptible to magnetic disturbances in the environment. Thus, the compasses must be calibrated for the magnetic effects in the environment after installation. The calibration also improves the compass accuracy. An analysis of the effects of magnetic disturbances showed that the ellipse output assumption in the Sampson-distance based ellipse fitting method could be used to fit raw magnetic compass measurements. Then, the fitting coefficients are used to calibrate the compass readings to compensate for the environmental disturbances to improve the heading accuracy. A multi-position turntable experiment was used to calibrate a C100 two-dimensional magnetic compass fabricated by KVH. After calibration, the mean heading error was reduced from 1.5° to 0.25°, which illustrates the validity and practicality of this method.

The extraction rate of protein extracted from biological tissue depends on the particle size of the biological tissue powder; therefore, the biological tissue particle sizes need to be smaller to improve protein extraction. Vibratory milling method is widely used for size reduction of biological tissue products with the milling product size and milling efficiency significantly affected by the milling process parameters, such as the collision frequency and impact energy. The effects of the milling parameters on the milling process were studied using the event driven method (EDM) to construct a dynamic model of one milling ball in a milling vial in a vibratory milling mechanism. The effects of the vibration frequency, the vibration amplitude and the milling vial diameter on the collision frequency and the impact energy were then investigated. Increases in the vibration frequency and amplitude increased the collision frequency and impact energy. For a fixed milling vial volume, an increase in the milling vial diameter reduced the collision frequency and the impact energy. The results provide a theoretical basis for milling mechanism design and parameter selection.

Group dynamics theory shows that social relationships are an important factor influencing group behavior. The characteristics and changes in social relationships during emergencies were studied in experiments modeling 6 high-rise building emergency evacuations. The evacuees are all from a graduate class with 30 students where are some pre-existing social relations such as friends, girl and boy friends, and roommates. Social network analyses, questionnaires and video data showed that the leader-follower behavior during emergencies may be influenced by the normal social relationships, but that the center of the evacuee group, the group cohesion, and subgroups showed obvious changes. A mutual believed degree (MBD) was defined to identify the opinion leaders in normal situations and in emergencies which shows that the potential leaders in emergencies are most likely from the normal opinion leaders and from the men who run faster in evacuations.