Good Time Performance Research Articles

Quantitative cyber-physical security analysis methodology for industrial control systems based on incomplete information Bayesian game

Industrial control systems (ICSs) are distributed complex cyber physical systems (CPSs). The core parts of ICSs are usually ultimate targets of attackers but being protected strictly and difficult to attack directly. Attackers tend to take cyber-physical attacks via multiple attack-steps from outer nodes with less protection. Then, threats propagate from one node to another until reaching the core assets or being detected. This paper develops a unified methodology which can model and analyze the cyber-physical attacks on ICSs quantitatively and automatically. We define the weighted colored Petri net and propose the basic cyber-physical attack models. Second, we propose the method to calculate the weights in attack models by modeling threat propagation between two nodes as mixed-strategy Bayesian attack-defense game with incomplete information and solving the refined Bayesian Nash Equilibrium. Results show that weights in the cyber-physical attack model (CPAM) are nearly stable even when parameters change. Moreover, we build threat propagation matrix and security state vector and further design the cyber-physical attack path analysis algorithm which can discover possible attack paths with specific attack losses. The case study verifies our methodology and evaluations show that our methodology has good time performance within limited node number. This work can help protect ICSs more efficiently.

Human motion tracking and positioning for augmented reality

AR (Augmented reality) is a research hotspot in the current computer application field. AR technology enhances people’s understanding and experience of the real environment by adding virtual objects to real scenes to integrate virtual objects with the real environment. Aiming at the weak processing power of intelligent terminals and the characteristics of limited hardware resources, this paper proposes a more effective human motion feature extraction and descriptor algorithm. The feature point detection and positioning method suitable for intelligent terminals is proposed in a targeted manner, which solves the problem of mismatching of similar structures. In addition, this paper proposes an AR-oriented recursive tracking algorithm for human motion. The positional relationship of the current frame is calculated from the position of the previous frame. A combination of ORB (Oriented fast and Rotated Brief) feature descriptors and KLT (Kanade-Lucas-Tomasi) algorithm is adopted. The ORB feature descriptor matched by the first frame image and the reference image is tracked by the KLT tracking algorithm, and the feature descriptor of the previous frame is tracked in the current frame, thereby eliminating the phenomenon of virtual object jitter. The experimental results show that the recursive tracking scheme has better performance in time and precision than the detection tracking scheme.

First results of the TIMESPOT project on developments on fast sensors for future vertex detectors

The TIMESPOT project aims at the construction of a mini-tracker demonstrator implementing both high space and time resolutions at the single pixel level. The pixels have a pitch of 55 × 55 μm2. Specified time resolution is equal or better than 50 ps. Developed sensors are based both on 3D silicon and diamond technologies, whose layout and fabrication process have been suitably optimized for best time resolution. Read-out pixel electronics is being developed in 28-nm CMOS technology. The first batch of 3D silicon sensors, containing several test structures based on different geometries of the electrodes, has been delivered in June 2019 and has been tested against its timing performance both under laser and minimum ionizing particle beams. In the present paper, the output of these starting measurements is presented. The sensors show very good timing performance, having σ¡30 ps (sigma), although operated with non-optimized front-end electronics and in noisy environment. These results represent an important step forward in the development of pixels with timing operating at extremely high interaction rates and fluences, as required in the next generation of upgraded colliders.

Improving superfluous load avoidance release (SLAR): A new load-based SLAR mechanism

A workload limit forms an essential part of most order release methods designed for high-variety make-to-order contexts, but this mechanism does not necessarily lead to the lowest possible direct load buffer. To counter this, the Superfluous Load Avoidance Release (SLAR) procedure was developed that avoided the use of a workload limit altogether. SLAR significantly improves performance compared to alternative release methods in the literature but has been criticized for being impractical as it can lead to uncontrolled loads at stations downstream in the routing of an order. This criticism can be overcome by introducing a workload limit. Using simulation, this study shows that introducing a limit to SLAR further reduces the superfluous direct load, specifically during high load periods. This not only controls the load at downstream stations but also yields a further reduction in the percentage of tardy jobs whilst maintaining SLAR's good mean tardiness and shop floor throughput time performance. Meanwhile, introducing an additional load-based trigger further improves mean tardiness performance. These results partly question one of SLAR's original design principles and extend the theory upon which SLAR is built, by linking each of SLAR's two release triggers to periods of low and of high load. Finally, by gaining control over the workload in periods of high load, the practical applicability of SLAR is substantially enhanced.

SECURITY IMPROVEMENTS OF INTERNET OF THINGS SYSTEMS

In this paper, we discuss the security of the Internet of Things (IoT) which needs to utilize particular algorithms in order to offer low power consumption and a long lifespan, along with other parameters such as strong immunity against attacks, lower execution time and acceptable performance. The present work considers a simple lightweight security encryption algorithm (LSEA) to be compatible with IoT systems, named (LSEA-IoT) for improving the security issue in IoT devices. The new LSEA-IoT scheme has be implemented for encrypting texts and images data and several tests such as visual checking, histogram diagrams, entropy and correlation analyses are carried out in order to ensure the suitability of the LSEA algorithm. Simulation results show that the performance of LSEA based IoT scheme achieves acceptable security level in terms of histogram, entropy and correlation performances as well as it has a good execution time performance for different types of messages. Based on this analysis, it can be concluded that the proposed LSEA-IOT is a promising algorithm and can be considered as an appropriate nominee to be utilized for IoT devices and it can be used for real-time applications.

Determining the Degree of Reliability of Energy Simulation Based on a Model of Organizational Capacity and Maturity

The maturity and capacity of a business are parameters that are directly associated to its quality and experience. In the specialized field of energy performance, these parameters are a determining factor on the reliability of results. In Chile, a lack of codes, standards or norms on energy simulation, it is of vital importance to have a system that is precise in its predictions. This study is centered on determining the level of uncertainty by analyzing different specialized firms in this field, taking into account their level of experience and internal capacities.We analyzed 32 consulting firms in function of their capacities and experience through a matrix of organizational capacity and maturity adapted to the energy efficiency field. To do this, we proposed the following elements of analysis: type of system used for simulation (static-dynamic), qualification of the staff in charge of each study, certification in national or international standards related to the specialty, use of internal work protocols and the existence of a quality assurance system for their processes.It can be expected that the level of reliability increases with firm maturity due to the ponderation that is assigned to each level. At the same time good performance is assured when there exist objective procedures that sustain internal capacities in such a way that the highest levels of predictability are achieved in an optimized atmosphere complemented with quality assurance systems.This tool can serve as a reference for firms in the project design phase, in such way that they can determine the confidence level of a simulation in function of the specialist’s quality and experience.

Interaction design research based on large data rule mining and blockchain communication technology

As the amount of information in the internet of things increases, data storage management tends to be distributed, which leads to problems such as difficult data cooperation and interaction between sites, low communication efficiency, and poor reliability. Blockchain is one of the new information technologies supporting the development of management information system. It provides a solution for the storage, verification, transmission, and exchange of distributed data. To optimize the communication performance from two aspects of communication topology and communication mechanism, a multi-link and concurrent communication tree model was constructed. To improve blockchain communication technology, an interactive design method based on big data rule mining and blockchain communication technology was proposed, which mainly solved the optimization of transmission performance of blockchain data. On the basis of ensuring the stability and reliability of data transmission, the efficiency of data transmission in blockchain was further optimized, and the integrated factor communication tree algorithm (IFT) was proposed. To solve the influence of transmission delay between nodes on communication performance, a multi-link multifactor weighted communication tree algorithm (MMWT) considering weight was proposed. Moreover, to improve the efficiency of data communication, ensure the reliability of transmission, and improve the fairness of service in the blockchain, different strategies for optimizing the performance of data communication in the blockchain were proposed under the constraints of factors such as node communication ability, node trust, weight, and service request priority. Finally, the simulation results showed that MMWT algorithm had good communication performance in concurrent communication time, communication tree reliability, communication tree depth, and other aspects.

Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking

A number of vision-based algorithms designed to detect and track unmanned aerial vehicles (UAVs) from on board a second UAV have been researched, implemented, and experimentally validated over the last decade. However, the successful methods have tended to rely on characteristics such as color or shape, meaning they require the target UAV to have particular markings or geometries. This paper uses the Viola–Jones cascade classifier, a computer vision algorithm originally designed to detect human faces in video streams, and demonstrates its capability for detecting and tracking an arbitrary type of UAV with excellent performance in either indoor or outdoor environments and with a variety of backgrounds. The Viola–Jones algorithm is applied to two specific quadrotor UAV models, the Solo from 3D Robotics and the AR.Drone 2.0 from Parrot. Experimental testing demonstrates that the resulting system achieves very good detection and tracking performance in real time on each UAV type for both indoor and outdoor flight tests.

Indoor place classification by building cardinal-direction prototyping blocks on point clouds

Making robots know people’s place concepts has attracted researchers for decades. People believe that this capability will firmly benefit not only robot–human interaction but also reasonable and social robot behaviors, or even traditional problems in robot research such as object detection. Focusing on place classification, this paper builds a kind of native pure 3D geometric description to capture place layouts based on common point clouds. This perspective enables our method to naturally accommodate various illuminations, including extremely bad lighting for which traditional image methods cannot work properly. The space of a place is first divided into 3D voxels. The cardinal orientations of this space are then extracted, and the geometric attributes of the voxels are subsequently represented based on the cardinal orientations. The voxels with geometric attributes are defined as the cardinal-direction prototyping blocks (CDPBs). Next, the CDPB distribution for a scene is calculated by qualitative spatial description technology, thereby obtaining the complete place description. Given the sparse description, the sparse random forest (SRF) is used for learning. The experiments indicate that the CDPB-based method outperforms the current 3D geometric method and its mixed method, and it has good time performance. The main advantages of our method are that it does not require any strict hypotheses on surfaces, such as planar surfaces, it requires smaller fusion windows to attain satisfactory classification rates, it can be used in extreme lighting environments, and its parameter selection is easy.

Effect of the Retarder for Grouting Materials in Pavement Reinforcement

Grouting is a common method for pavement reinforcement. In order to improve the characteristics of the slurry, additives such as retarder and water reduce agent, are mixed with the slurry. This paper was focused on effect of the retarder for grouting materials in pavement reinforcement. Several tests have been done to reveal the effect of mass of retarder for setting time, flowability and strength. According to the result of the test, the best choice of retarders for a pavement grouting is the retarder e (NMS) for its good performance in the setting time, flowability, compressive strength and the price.

Efficient Privacy-Preserving Anomaly Detection and Localization in Bitstream Video

In cloud computing, videos may be in an encrypted format to protect privacy. Therefore, encrypted video processing is an important application in secure cloud computing. In this paper, we focus on parameter estimation and anomaly detection in an encrypted video bitstream. By analyzing the common properties of video encoding frameworks and the format-compliant encryption schemes, we propose an anomaly detection scheme for encrypted video bitstream with format-compliant encryption. From the encrypted bitstream, we extract three types of complementary features, i.e., the macroblock sizes, the macroblock partitions, and the motion vector difference magnitude, and then propose a method to combine these three features. The proposed detection and localization scheme does not involve video decryption, full decompression, or an interactive protocol, which makes it efficient. Our scheme is also compatible with different video encryption methods. To accelerate the running time, we develop a parallel implementation for our scheme. The experimental results show that our method achieves good running time and detection rate performance.

PENGGUNAAN STANDARD WBS (WORK BREAKDOWN STRUCTURE) PADA PROYEK BANGUNAN GEDUNG

WBS (Work Breakdown Structure) plays an important role in every construction project. WBS is a hierarchy of decreasing scope of work to become the smallest level called a work package, making it easier in the process of project management and control. The current problem is that many contractors in Indonesia, both large and small contractors, do not understand the importance of using WBS in a construction project. This causes project performance to be less than optimal, especially on cost and time performance. Currently, WBS standards have been developed for building projects. In this study will look at the cost and time performance of the project from the use of WBS standard building. The sample used in this study are projects that have applied the use of project standards. The projects include Building X, Lecture Building Y, and Sports Facility Building Z. The method used in this study is the EVM (Earn Value Method) approach, collecting data using questionnaires. The results showed that using the WBS standard had an effect on cost and time performance. Good cost and time performance is shown in the Building X and Lecture Y buildings. While the cost and time performance of the Z Sports Facility Building tends to be unstable. This is because the work items that exist in the WBS standard are not in accordance with the project conditions, so it is necessary to adjust work items.

Multiple Node Upset-Tolerant Latch Design

This paper proposes a general method for the design of multiple node upset (MNU)-tolerant latches. First, two double node upset (DNU)-tolerant latches and one triple node upset (TNU)-tolerant latch are introduced. These proposed latches are highly resilient to DNUs and TNUs in terms of their output nodes. Then, a generalized MNU-tolerant latch structure is introduced based on the construction features of these latches. Massive Hspice simulations using silicon-on-insulator (SOI) technology indicate that none of the proposed latches would output an unrecoverable error. Finally, we compare our latches to the designs in other reports. The DNU-tolerant latch, DNUTL-1, demonstrates considerable advantages in terms of the area, propagation delay, power, and the delay-power-area product. Moreover, the DNUTL-1 master–slave flip-flop has good performance in setup time, hold time, and latching window.

Cloud Service Security Adaptive Target Detection Algorithm Based on Bio-Inspired Performance Evaluation Process Algebra

Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra (Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.

Hybrid Type-2 Fuzzy Based Channel Estimation for MIMO-OFDM System with Doppler Offset Influences

The channel estimation methods track and predict the variation in channel characteristics, so that the original signal can be obtained after nullifying the channel induced influences. The channel estimation methods impact the overall performance of the MIMO-OFDM system. When the communicating nodes are mobile, a complete estimation of the fast time varying channel is accomplished if the Doppler offset is evaluated along with the channel gain. However, most of the channel estimation approaches proposed in literature for MIMO-OFDM systems assume that the Doppler offset contributed by highly mobile communicating nodes is already known to the receiver. The estimation of the Doppler offset with the channel coefficients renders the channel estimation problem non linear. In this paper, the issue of this non linear channel estimation for high mobility communicating nodes with associated dynamic Doppler offset in a MIMO-OFDM system is addressed. In order to obtain complete information of the channel which includes the channel coefficients and the associated Doppler offsets, a hybrid interval type-2 fuzzy aided Kalman filter for channel estimation is proposed. The type-2 fuzzy based membership functions are used here opposed to the type-1 fuzzy membership functions because the type-2 fuzzy membership functions are capable of effective modeling even under high degree of uncertainties. Furthermore, a detailed computational complexity analysis of the proposed algorithm is presented which shows that the algorithm has moderate computational complexity and has good performance in fast time varying channel conditions with high node mobility in a MIMO-OFDM system.

A contact algorithm for voxel-based meshes using an implicit boundary representation

Numerical methods operating on structured grids have become popular since they offer good run time performance and are able to process directly voxel-based digital data from image recordings. Hence, the general framework of these fast solvers presupposes an unfitted boundary approximation avoiding complicated meshing of bodies. This allows an efficient handling with geometrical issues. Nevertheless, contacts between deformable solids are hard to deal with in the presence of this boundary representation. For this difficulty we suggest the usage of an implicit boundary representation combined with a modified saddle point formulation, resembling Nitsche’s approach. Both ideas give an elegant approach for discretizing the contact terms and enable a simple contact detection. Moreover, we suggest an intermediate surface as new reference contact area which fits well into our proposed method. In the end we present numerical results and analyze the accuracy and convergence rate. Furthermore, we demonstrate the current application range of our approach for problems with multiple contacts. In this paper we focus only on frictionless contact with small deformations.

A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching

This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular value decomposition (SVD) and Kurtosis criterion. Based on the assessment results, a kind of deep learning algorithm, i.e., stacked denoising autoencoder (SDAE), is introduced to extract the common deep features of normal state and early fault state. Support vector data description (SVDD) is applied to establish the offline detection model using the obtained features. At the online stage, a self-adaptive matching strategy with 1-Dimensional anchor is proposed. By utilizing the SDAE model established at offline stage, this strategy can extract more representative deep features of the target bearing via generating various proposal fragments and then determining the fault occurrence time in a self-adaptive way by feeding the online features into the SVDD model. Experiments run on the bearing data set of IEEE prognostic and health management (PHM) Challenge 2012. The results show the proposed approach has good detection performance in real time and much lower false alarm rate, with no need to acquire fault characteristic frequency in advance.

A novel bowl-shaped microchannel plate with high electron collection efficiency and good time performance

Abstract Owing to the severe loss of photoelectrons striking at the input electrode of conventional microchannel plate (MCP), photoelectron collection efficiency (CE) of devices based on traditional MCP fluctuates around the MCP open area ratio and cannot make a breakthrough. Photoelectron backscattering from the MCP surface also causes late pulses smearing timing and spatial performance. In this work, a novel bowl-shaped MCP with an open area ratio higher than 90% whose input electrode is coated by a thin film with high secondary electron emission yield (SEY) is proposed as an effective approach to improve CE and suppress late pulses. A three-dimensional MCP-PMT model is developed in CST Studio Suite to evaluate CE and time performance of the bowl-shaped MCP, and compared with the traditional one. Results show that CE of the PMT based on the bowl-shaped MCP is nearly 100% and the delayed pulse is less than 2%. This indicates that good temporal and spatial resolution can be achieved with the bowl-shaped MCP.

A Self-Adapted Swarm Architecture to Handle Big Data for “Factories of the Future”

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. This poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. The challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big Data analytics. This paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. The work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector.

Implementation of the TOPKAPI model in South Africa: Initial results from the Liebenbergsvlei catchment

Flash floods and droughts are of major concern in Southern Africa. Hydrologists and engineers have to assist decision makers to address the issue of forecasting and monitoring extreme events. For these purposes, hydrological models are useful tools to: Identify the dominant hydrological processes which influence the water balance and result in conditions of extreme water excess and / or deficit Assist in generating both short- and long-term hydrological forecasts for use by water resource managers. In this study the physically-based and fully distributed hydrological TOPKAPI model (Liu and Todini, 2002), which has already been successfully applied in several countries in the world (Liu and Todini, 2002; Bartholomes and Todini, 2005; Liu et al., 2005; Martina et al., 2006), is applied in Africa for the first time. This paper contains the main theoretical and numerical components that have been integrated by the authors to model code and presents details of the application of the model in the Liebenbergsvlei catchment (4 625 km2) in South Africa. The physical basis of the equations, the fine-scale representation of the spatial catchment features, the parsimonious parameterisation linked to field / catchment information, the good computation time performance, the modularity of the processes, the ease of use and finally the good results obtained in modelling the river discharges of Liebenbergsvlei catchment, make the TOPKAPI model a promising tool for hydrological modelling of catchments in South Africa.