Optimal Replacement Time of Electrical Components Based on Constant-Interval Replacement Model: Equipment Inspection Method and Weibull Analysis

The operation of distribution system with the components in deteriorating condition makes the system reliability worsen. It is important to find the solution for balancing failure cost and maintenance benefits such as down-time and reliability. In this paper, time to replace the components in optimum condition based on constant-interval replacement mode is investigated. The optimal replacement time is mainly depended on component’s reliability and the cost ration of preventive replacement and failure replacement. In this paper, equipment inspection method and Weibull Analysis is applied to obtain the accurate reliability estimation. Weibull Analysis is applied with constant-interval replacement model to investigate the optimum replacement time for each component considering the different cost ratios. According to the quantitative results, the determination of the optimal replacement time (OPT) can minimize the total downtime and failure cost. Consequently, the reliability of the system is maximized and estimation also becomes more accurate due to sufficient approach.

Aspects of Replayability and Software Engineering: Towards a Methodology of Developing Games

One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.

Application of Fourier Series Expansion Method with PMLs to the Microcavities on Two-Dimensional Photonic Crystals

By using a Fourier series expansion method combined with Chew＇s perfectly matched layers （PMLs）, we analyze the frequency and quality factor of a micro-cavity on a two-dimensional photonic crystal is analyzed. Compared with the results by the method without PML and finite-difference time-domain （FDTD） based on supercell approximation, it can be shown that by the present method with PMLs, the resonant frequency and the quality factor values can be calculated satisfyingly and the characteristics of the micro-cavity can be obtained by changing the size and permittivity of the point defect in the micro-cavity.

Study of Material Evaluation Probe Using a Longitudinal Wave and a Transverse Wave

Transmitting a longitudinal wave and a traverse wave into a composite material in a molten state has been studied in the online control of the composite material which cannot be evaluated by a conventional ultrasonic sensor as a final analysis, using the difference in the propagation characteristics of both modes. It is especially expected that measurement of the physical quantity which was not able to be conventionally measured can be performed by carrying out coincidence measurement of the ultrasonic wave in both modes. Therefore, in this research study, an ultrasonic probe, which can simultaneously transmit and receive a longitudinal wave and a traverse wave has been developed using an electromagnetic acoustic transducer (EMAT) because it has the advantage of measuring high temperature samples. In this study, two methods have been compared. The 1st method uses a traverse wave EMAT that travels in a vertical direction and a bar wave by which the low order mode is equivalent to longitudinal wave vibration. The other method is to carry out the mode conversion of the traverse wave by a traverse wave-EMAT. The longitudinal converted from the transverse wave are spread in the axis direction. As the experimental results of both optimizations of the drive conditions, it has been confirmed that the 2nd mode conversion method was promising. This paper reports about the trial process and the experimental results.

Economic Analysis and Environmental Impacts of a Hybrid PV System in Arid Climate Considering Different Types of Solar Trackers

This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.

Comparison of MRI Under-Sampling Techniques for Compressed Sensing with Translation Invariant Wavelets Using FastTestCS: A Flexible Simulation Tool

A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.

A robust deformed convolutional neural network(CNN)for image denoising

Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image denoising.However,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image denoising.Using relations of surrounding pixels can effectively resolve this problem.Inspired by that,we propose a robust deformed denoising CNN(RDDCNN)in this paper.The proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding pixels.The EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed RDDCNN.To address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean image.Besides,we implement a blind denoising model.Experimental results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative analysis.Codes can be obtained at https://github.com/hellloxiaotian/RDDCNN.

强抗泄漏的无条件安全动态秘密共享方案

(t,n)门限秘密共享中,分发者将秘密S分成n个秘密份额,使得任意不少于t个秘密份额可以成功重构秘密S,而小于门限t的秘密份额无法获得有关秘密的任何信息.为提高份额的机密性,减少验证份额过程所需的计算开销,增强秘密共享方案的防欺骗性、安全性、实用性以及抗泄漏性,本文提出了一种强抗泄漏的无条件安全动态秘密共享方案:(1)方案引入信息传递环和插值因子等技术作为重构通信方式和重构信息,有效提高份额的机密性;(2)方案分别为每个参与者分配一对验证钥以验证重构秘密的一致性,避免验证秘密份额的复杂过程,方案与可验证秘密共享方案具有相同的抗欺骗能力,可有效防止n-1参与者联合欺骗,较一般防欺骗方案(达到n/2或n/3防欺骗能力)有更强的抗欺骗能力;(3)方案安全性与敌手的计算能力无关,达到无条件安全性;(4)方案无需要更改秘密份额即可实现分享秘密的更新,达到动态性;(5)本文首次提出秘密共享中的最大泄漏率概念,用以描述秘密共享中最大可泄漏的秘密份额个数与总秘密份额个数的关系,可作为评价秘密共享机制中抗泄漏能力强弱的标准参数之一.

医疗大数据隐私保护多关键词范围搜索方案

随着医疗信息系统的急速发展,基于医疗云的信息系统将大量电子健康记录(EHRs)存储在医疗云系统中,利用医疗云强大的存储能力和计算能力对EHRs数据进行安全与统一的管理.尽管传统加密机制可以保证医疗数据在半诚实云服务器中的机密性,但对加密后的EHRs数据执行安全、快速、有效的范围搜索,仍是一个有待解决的关键问题.提出一种支持多关键词范围搜索的可搜索加密方案:利用向量积保持加密机制实现复杂查询结构的可搜索加密,可支持连接关键词查询、范围查询以及通配符的查询;通过随机化构建搜索索引和搜索陷门,实现搜索模式隐藏,达到搜索语句的隐私保护;采用矩阵哈达马积缩小所需密钥矩阵的维度.理论分析和实验结果表明:该方案在达到医疗数据隐私保证的同时,对用户的检索策略也进行了有效的隐私性保护,有效提高了检索效率,降低了创建索引及陷门所用时间,实现了多用户多文件下医疗数据的范围搜索能力.

Asynchronous Secret Reconstruction and Its Application to the Threshold Cryptography

In Shamir’s(t,n) threshold of the secret sharing scheme, a secret is divided into n shares by a dealer and is shared among n shareholders in such a way that (a) the secret can be reconstructed when there are t or more than t shares;and (b) the secret cannot be obtained when there are fewer than t shares. In the secret reconstruction, participating users can be either legitimate shareholders or attackers. Shamir’s scheme only considers the situation when all participating users are legitimate shareholders. In this paper, we show that when there are more than t users participating and shares are released asynchronously in the secret reconstruction, an attacker can always release his share last. In such a way, after knowing t valid shares of legitimate shareholders, the attacker can obtain the secret and therefore, can successfully impersonate to be a legitimate shareholder without being detected. We propose a simple modification of Shamir’s scheme to fix this security problem. Threshold cryptography is a research of group-oriented applications based on the secret sharing scheme. We show that a similar security problem also exists in threshold cryptographic applications. We propose a modified scheme to fix this security problem as well.

Pipe Inspection System by Guide Wave Using a Long Distance Waveguide

In the industrial fields, many high temperature structures that require a non-destructive inspection exist. However, there are currently few sensors that can carry out non-destructive testing in a high temperature environment. In particular, the ultrasonic sensor is normally not used at over 50 degrees Celsius. Also, a special sensor for high temperature is currently available, but there are various constraints;it has not yet reached a level that is useful in industry. Therefore, we have been developing a new sensor system using a long waveguide which can transmit an ultrasonic wave from a long distance. Especially, this study focuses on applying the developed technique to a pipe which is used in a nuclear power plant. Therefore, the best rectangular-shaped waveguide was studied and attempted to be wound around a pipe to be driven by an acoustic source of a guide wave. Finally, the L (0, 2) and T (0, 1)-mode guide waves were successfully detected by optimizing the shape of the opposite edge of the rectangular-shaped waveguide that could detect the reflected signal from an artificial defect machined into a test pipe.

PUF-Based Key Distribution in Wireless Sensor Networks

Physical Unclonable Functions(PUFs)can be seen as kind of hardware one-way functions,who are easily fabricated but difficult to clone,duplicate or predict.Therefore,PUFs with unclonable and unpredictable properties are welcome to be applied in designing lightweight cryptography protocols.In this paper,a Basic Key Distribution Scheme(Basic-KDS)based on PUFs is firstly proposed.Then,by employing different deployment modes,a Random Deployment Key Distribution Scheme(RD-KDS)and a Grouping Deployment Key Distribution Scheme(GD-KDS)are further proposed based on the Basic-KDS for large scale wireless sensor networks.In our proposals,a sensor is not pre-distributed with any keys but will generate one by the embedded PUF when receiving a challenge from the gateway,which provides perfect resilience against sensor capture attacks.Besides,the unclonable and unpredictable properties of PUF guarantee the key uniqueness and two-way authentication.Analysis and experiment results show that our proposals have better performances in improving the resilience,secure-connectivity,and efficiency as compared to other schemes.

Smart phone-based context-aware augmentative and alternative communications system

A smartphone-based context-aware augmentative and alternative communication(AAC) was applied was in order to enhance the user's experience by providing simple, adaptive, and intuitive interfaces. Various potential context-aware technologies and AAC usage scenarios were studied, and an efficient communication system was developed by combining smartphone's multimedia functions and its optimized sensor technologies. The experimental results show that context-awareness accuracy is achieved up to 97%.

Ultrasonic Inspection System Using a Long Waveguide with an Acoustic Horn for High-Temperature Structure

The nondestructive inspection of a high temperature structure is required in order to guarantee its safety. However, there are no useful sensors for high temperature structures. Some of them cannot work at temperatures over 50°C. Another concern is that they are too expensive to use. A sensing system, which can transmit and receive an ultrasonic wave that travels a long distance using a long waveguide, has been studied. We confirmed that the optimal guided ultrasonic wave could travel more than 10 m using an electromagnetic transducer (EMAT) with a thin Ni-sheet surrounded on the surface of the bar and a 2-mm diameter bar as the waveguide. However, we had the difficult problem of receiving the reflected ultrasonic wave from the inside of a test specimen. We tried to improve the trial inspection system using an acoustic horn. An experiment in which the temperature of the test block was heated to about 500°C has now been completed. Finally, the condition of the bend in the waveguide to pass without reflection was confirmed.

A Data Analysis Framework for Earth System Simulation within an <i>In-Situ</i>Infrastructure

This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.

High-Performance Magnetic-core Coils for Targeted Rodent Brain Stimulations

Objective and Impact Statement.There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms.We describe a novel rodent coil design to improve the focality for targeted stimulations in small rodent brains.Introduction.Transcranial magnetic stimulation(TMS)is becoming increasingly important for treating neuropsychiatric disorders and understanding brain mechanisms.Preclinical studies permit invasive manipulations and are essential for the mechanistic understanding of TMS effects and explorations of therapeutic outcomes in disease models.However,existing TMS tools lack focality for targeted stimulations.Notably,there has been limited fundamental research on developing coils capable of focal stimulation at deep brain regions on small animals like rodents.Methods.In this study,ferromagnetic cores are added to a novel angle-tuned coil design to enhance the coil performance regarding penetration depth and focality.Numerical simulations and experimental electric field measurements were conducted to optimize the coil design.Results.The proposed coil system demonstrated a significantly smaller stimulation spot size and enhanced electric field decay rate in comparison to existing coils.Adding the ferromagnetic core reduces the energy requirements up to 60%for rodent brain stimulation.The simulated results are validated with experimental measurements and demonstration of suprathreshold rodent limb excitation through targeted motor cortex activation.Conclusion.The newly developed coils are suitable tools for focal stimulations of the rodent brain due to their smaller stimulation spot size and improved electric field decay rate.

Joint fuzzy background and adaptive foreground model for moving target detection

Moving target detection is one of the most basic tasks in computer vision.In conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)framework.MD utilizes foreground information to facilitate background recovery.MF uses noise-based weights to fine-tune the background.So both noise and foreground information contribute to the recovery of the background.To jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is designed.The fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background estimation.To describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint adaptively.The temporal constraint is adjusted adaptively.Both foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability distribution.The experimental results of vast real videos illustrate the effectiveness of our method.Compared with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate foreground.Anti-noise experiments show the noise robustness of our method.

Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

Origin of strong and narrow localized surface plasmon resonance of copper nanocubes

Inexpe nsive copper nano particles are generally thought to possess weak and broad localized surface plasm on resonance(LSPR).The,present experimental and theoretical studies show that tailoring the Cu nanoparticle to a cubic shape results in a single intense,narrow,and asymmetric LSPR line shape,which is even superior to round-shaped gold nanoparticles.In this study,the dielectric function of copper is decomposed into an interband transition component and a free-electron component.This allows interband transition-induced plasmon damping to be visualized both spectrally and by surface polarization charges.The results reveal that the LSPR of Cu nanocubes originates from the comer mode as it is spectrally separated from the interb and transitions.In additi on,the interband tran sitions lead to severe damping of the local electromagnetic field but the cubic corner LSPR mode survives.Cu nanocubes display an extinction coefficient comparable to the dipole mode of a gold nanosphere with the same volume and show a larger local electromagnetic field enhancement These results will guider-development of in expensive plasmonic copper-based nano materials.