Electrified railway is one of the major users of electricity in the power system. The access of energy storage system (ESS) effectively solves the problems of peak traction load (TL) shock and regenerative braking energy (RBE) recycling, and the access of photovoltaic (PV) system improves environmental and economic benefits. However, in the current control strategy based on the electrified railway with ESS and PV, there are still problems of less optimal overall peak clipping effect of TL and insufficient penetration of renewable energy sources (RES) due to low utilization and lack of pre-processing capacity of ESS. Therefore, this paper proposes a rolling-adaptive peak clipping (RAPC) control strategy coordinating RBE recycling and PV consumption. Firstly, perform rolling peak clipping through dynamically divided implementation windows to make full use of the available energy in each time period. Secondly, according to the state of charge (SOC) of ESS and the specific working condition of TL, the ESS output is modified accordingly to perform adaptive deep peak clipping maximizing the consumption of PV energy and RBE. The simulation verified that the proposed strategy can improve the overall peak-clipping effect of TL, and achieve full recovery of RBE and complete consumption of PV energy under the same ESS configuration compared to the global optimization method. The results show that the RAPC control strategy can realize optimal peak-clipping and RBE utilization, and increase the PV penetration with limited ESS capacity, thus improving the economic benefits of the traction power supply system (TPSS) and promoting the energy transition of TPSS.

With the rapid development of high-speed and heavy-load electrified railway, the peak impact and the regenerative braking energy content of traction load become increasingly significant, which has become an important problem affecting the construction and operation benefits of electrified railway. On the basis of comprehensively solving the power quality problems of electrified railway, a multi-application strategy based on the railway static power conditioner (RPC) with the energy storage system (ESS) is proposed in this paper to improve the economic benefits of the traction system. Through the implementation of the peak clipping and the recuperation and utilization of regenerative braking energy of traction load with the guidance of such strategy, the comprehensive economic benefits of the traction system can be improved. By analyzing the mathematical relationship between the comprehensive economic benefits and the parameters such as peak clipping rate and regenerative braking energy utilization rate of the system, an optimization model with the goal of maximizing the comprehensive economic benefits is established. Therefore, the optimal configuration capacity of the ESS is obtained. Finally, the experiments are carried out in MATLAB. The results show that, with the multi-application strategy, the peak load clipping and the recuperation and utilization of regenerative braking energy can be simultaneously realized. It is also demonstrated that the comprehensive economic benefits and environmental benefits can be improved while improving energy efficiency.

Traction load prediction is the key part in operation planning of electrified railway. In order to better dig effective information from massive traction load data and improve the prediction accuracy and training speed, a new traction load prediction method is designed according to the characteristics of time sequence, random fluctuation and no-load happens frequently of traction load. In this method, a CNNTCN joint model is proposed by combining Convolutional Neural Networks (CNN) and Temporal Convolutional Networks (TCN). The abundant traction load data are adopted as the input of the joint model. First, CNN is used to extract local features and generate continuous feature maps. The output of the last feature layer of CNN is taken as the input of TCN, and then the TCN is used for traction load prediction. The proposed method is used to conduct load prediction experiments with the traction load data of the power supply arms in a certain traction substation. The experimental results show that the proposed method has faster training speed and higher prediction accuracy than the current prediction methods in the field of prediction algorithm.

This paper presents an optimal collaborative scheduling method to relieve line overloads during the process of emergency faults. First, the theoretical analysis of power flow control via the voltage source converter based high voltage direct current (VSC-HVDC) system, generator output or load curtailment is investigated. Then, a collaborative scheduling framework is designed based on these two measures, with the comprehensive considerations of total emergency control cost for different steps. Finally, the proposed framework is assessed from the perspectives of performance and economy using a modified New England power system, and the results are compared with two methods. Numerical test results show that the proposed method is capable of eliminating line overloads in an economic way.

With the development of power scale of railway traction load, power quality problems of traction power supply system have also been widely concerned. Railway Power Conditioner (RPC) can comprehensively solve power quality problems and has become a current hot research topic. How to reduce the RPC capacity while satisfying the power quality simultaneously is the urgent problem to be settled. The existing method is to set a fixed load value when calculating the compensation capacity in the optimization algorithm, while the randomness and uncertainty of real load fluctuations were not considered. It will probably cause a bad compensation consequence with an insufficient compensation capacity or a poor cost performance with a huge compensation capacity. To deal with this problem, a Multi-scenario Analysis method considering the uncertainty of traction load is introduced while calculating the RPC capacity in this paper. Optimization algorithm combined with K-means Clustering Algorithm is used to cluster the sampled massive data to achieve several operational scenarios and to calculate optimization compensation capacity of each scenario, and finally, the compensation capacity suitable for every scenario is obtained. This method can improve the economy of RPC while meeting the compensation requirements. The method is calculated and verified in MATLAB and further comparison and analysis have made, results show the feasibility and validity of the method used in this paper.

Two-Phase Stationary-Frame Enhanced Phase Locked Loops (αβ-EPLL) are largely employed for the synchronization of the grid-connected power electronic system. But when the grid voltage is disturbed with frequency change and harmonics, it cannot obtain accurate information under that changeable conditions. For the purpose of getting the three phase grid information accurately despite distort grid, a new αβ-EPLL attaches multiple delayed signal cancelation (MDSC) filters is raised in this paper. The proposed PLL possesses a faster transient response and higher stability margins. The usefulness of the raised method is validated by simulation results and experimental results.

2nd-order attacks utilize power values at two leakage points to break cryptographic systems protected by 1st-order random masking. Without profiling, the attacker do not know the exact location of the two leakage points. Standard 2nd-order attacks with an exhaustive search over two windows of size nw has computational complexity O(nw2) and does not scale well with the window size nw. We propose to apply a decision-combination attack, the majority vote (MV) attack, to combine 2nd order attacks at multiple candidate pairs of leakage points selected through two filters. The first filter pre-process the power traces with Fast Fourier Transformation (FFT) techniques and reduce the complexity to O(nwlog2(nw)). The second filter use an advanced statistical feature selection procedure, Higher Criticism (HC), to select leakage candidates that improve the effectiveness of decision-combination MV attack and other leakage-combination attacks. We derive theoretical success conditions of MV attacks as well as the typical maximum attack and a leakage-combination sum attack. The theoretical conditions are confirmed through performance comparisons of the attacks on synthetic data sets and on two real data sets, an FPGA implementation and a software implementation of masked AES. The proposed FF-HC-MV attack is data-adaptive, working well in all data sets.

Many to many (M2M) recommendation is one of the fundamental and important problems in commerce. With respect to this idea, profits, clients and products are inseparable. The traditional Top-N method cannot process the M2M recommendation problem. Therefore, this paper deals with the M2M recommendation as a many to many assignment problem via the group multi-role assignment (GMRA). Based on the concise formalization of Role-based collaboration (RBC) and its E-CARGO model, a successful approach using an (Extended Integer Linear Programming) x-ILP planning method and an improved greedy Top-N algorithm is proposed. These methods are verified by simulation experiments. Their results indicate the practicability of both solutions. As a comparison, the greedy Top-N method is faster than the x-ILP planning method via the PuLP linear planning package of Python. On the hand, the latter outperforms the former in recommendation accuracy.

As the new hash standard, Keccak-based secure hash function (SHA-3) will be used in various cryptographic applications. Its security will be of paramount importance to the systems built on top of it. This paper proposes efficient algebraic fault analysis (AFA) methods, and for the first time, applies them to all four modes of SHA-3 under relaxed fault models. Our AFA utilizes the clear algebraic properties of Keccak operations and is very suitable for the fault analysis of SHA-3. Both our analysis and experimental results show that the proposed AFA method is more efficient than the traditional differential fault analysis (DFA) under the single-byte fault model, requiring much fewer faults to recover a whole internal state of the hashing computation. Meanwhile, as AFA is able to exploit all the information available, it can be applied to SHA-3 modes with shorter digests and under more relaxed fault models, where often times the DFA method fails. Our results show that AFA can successfully break all the four SHA-3 modes under a 16-bit fault model, and break SHA3-512 under an even more relaxed fault model, 32-bit fault, all within several minutes. The successful AFA on SHA-3 demonstrates the vulnerability of Keccak algorithms to fault analysis, calling for protections against fault injection and fault analysis.

A novel dataset for benchmarking image-based localization is presented. With increasing research interests in visual place recognition and localization, several datasets have been published in the past few years. One of the evident limitations of existing datasets is that precise ground truth camera poses of query images are not available in a meaningful 3D metric system. This is in part due to the underlying 3D models of these datasets are reconstructed from Structure from Motion methods. So far little attention has been paid to metric evaluations of localization accuracy. In this paper we address the problem of whether state-of-the-art visual localization techniques can be applied to tasks with demanding accuracy requirements. We acquired training data for a large indoor environment with cameras and a LiDAR scanner. In addition, we collected over 2000 query images with cell phone cameras. Using LiDAR point clouds as a reference, we employed a semi-automatic approach to estimate the 6 degrees of freedom camera poses precisely in the world coordinate system. The proposed dataset enables us to quantitatively assess the performance of various algorithms using a fair and intuitive metric.

This paper presents an efficient algebraic fault analysis on all four modes of SHA-3 under relaxed fault models. This is the first work to apply algebraic techniques on fault analysis of SHA-3. Results show that algebraic fault analysis on SHA-3 is very efficient and effective due to the clear algebraic properties of Keccak operations. Comparing with previous work on differential fault analysis of SHA-3, algebraic fault analysis can identify the injected faults with much higher rates, and recover an entire internal state of the penultimate round with much fewer fault injections.

Side-channel attack utilizes side-channel leakages to extract the secret in crypto systems. Various countermeasures for different algorithms and platforms have been proposed to protect crypto systems against such attacks. Manual countermeasure design requires deep understanding of the target algorithm and implementation, and oftentimes is platform-specific and error-prone. In this paper, we propose the construction of Threshold Implementation (TI), a provably secure countermeasure against power attacks, as an automated compiler pass in the open LLVM (Low Level Virtual Machine) framework. Attack results show that the automatically generated TI designs are secure against power attacks. As our proposed scheme implements the countermeasure at the intermediate representation (IR) level, our method can be applied to any cipher software in any programming language, and the generated implementations can be ported to different platforms and architectures.

Feature coding and vector pooling are essential for image recognition in bag-of-visual-words (BoW) method. Encoding the low-level feature to rich one and pooling it without any information loss are very challenging works. In this paper, generalized pooling pyramid with hierarchical dictionary sparse coding is introduced to get rich sparse codes and alleviate the information loss in the phase of pooling. It includes two modules: First, with the low-level feature, hierarchical dictionary is learned for sparse coding to generate the hierarchical sparse representation. Second, in the phase of vector pooling, we present generalized pooling pyramid by utilizing the probabilistic function to model the statistical distribution of sparse codes. In the generalized pooling pyramid, the Fisher vectors which are computed with Gaussian Mixture (GMM) in different levels, are fused to represent the images. The performance of our method outperforms state-of-the-art performance in a large number of image categorization experiments on the event dataset (UIUC-Sport dataset) and the object recognition dataset (Caltech101 dataset).

Accurately predicting driving service orders in different regions is an essential task for service companies, in order to improve the service quality. In this paper, a specific ensemble multi-view prediction framework is proposed to address this task. It ensembles several different multi-view-based models with a weighted linear combination. Specifically, we have designed three specific multi-view-based models, which of them contains two types of views. In first view, a spatio-temporal prediction model (ST-Model) is employed to construct the features on historical orders. In second view, a specific CRF is designed to joint adjacent regions to collaborate predict future orders in these regions. The two views are learned and inferred simultaneously. Extensive evaluations on real order data in Beijing show that the proposed framework outperforms all baselines and participated multi-view-based models significantly in terms of mean absolute error (MAE) and root mean square error (RMSE).

Most news recommendation methods focus on using textual information of news to solve data sparseness problem of collaborative filtering. While if the text is not informative enough, these methods can't work well. A collaborative model combining matrix factorization, topic analysis and knowledge graph representation is proposed by introducing the knowledge from external knowledge base to alleviate the deficiency of the text. The experiment conducted on real life news dataset shows that the joint model outperforms the state-of-the-art method by 14% in Recall@200 metric, and improves the recommendation performance on sparse items by 20%.

The security of SHA-3 against different kinds of attacks are of vital importance for crypto systems with SHA-3 as the security engine. In this paper, we look into the differential fault analysis of SHA-3, and this is the first work to conquer SHA3-224 and SHA3-256 using differential fault analysis. Comparing with one existing related work, we relax the fault models and make them realistic for different implementation architectures. We analyze fault propagation in SHA-3 under such single-byte fault models, and propose to use fault signatures at the observed output for analysis and secret retrieval. Results show that the proposed method can effectively identify the injected single-byte faults, and then recover the whole internal state of the input of last round χ operation (χi22) for both SHA3-224 and SHA3-256.

Cryptographic systems are vulnerable to random errors and injected faults. Soft errors can inadvertently happen in critical cryptographic modules and attackers can inject faults into systems to retrieve the embedded secret. Different schemes have been developed to improve the security and reliability of cryptographic systems. As the new SHA-3 standard, Keccak algorithm will be widely used in various cryptographic applications, and its implementation should be protected against random errors and injected faults. In this paper, we devise different parity checking methods to protect the operations of Keccak. Results show that our schemes can be easily implemented and can effectively protect Keccak system against random errors and fault attacks.

Side-channel attacks have been a serious threat to the security of embedded cryptographic systems, and various countermeasures have been devised to mitigate the leakages. Power balance technologies such as wave dynamic differential logic (WDDL) aim to balance the power by introducing differential logic. However, different routing length leads to different capacitance of wire, and this hampers the strength of the power balance countermeasure. In this paper, we further balance the power of differential signals by manipulating the lower level primitives and placement constraints on a Field Programmable Gate Array (FPGA). We choose Advanced Encryption Standard (AES) as the encryption algorithm and apply Hamming weight model to demonstrate the amount of leakage for different implementations. Results show that our method not only efficiently mitigates the side-channel leakage but also saves FPGA logic block resources and dynamic power consumption.

Side-channel attacks have been a real threat against many embedded cryptographic systems. A commonly used algorithmic countermeasure, random masking, incurs large execution delay and resource overhead. The other countermeasure, operation shuffling or permutation, can mitigate side-channel leakage effectively with minimal overhead. In this paper, we target automatically implementing operation shuffling in cryptographic algorithms to resist against side-channel power analysis attacks. We design a tool to detect independence among statements at the source code level and devise an algorithm for automatic operation shuffling. We test our algorithm on the new SHA3 standard, Keccak. Results show that the tool effectively implements operation-shuffling to reduce the side-channel leakage significantly, and therefore can guide automatic secure cryptographic software implementations against differential power analysis attacks.

Graphics Processing Units (GPUs) have been used to run a range of cryptographic algorithms. The main reason to choose a GPU is to accelerate the encryption/decryption speed. Since GPUs are mainly used for graphics rendering, and only recently have they become a fully-programmable parallel computing device, there has been little attention paid to their vulnerability to side-channel attacks. In this paper we present a study of side-channel vulnerability on a state-of-the-art graphics processor. To the best of our knowledge, this is the first work that attempts to extract the secret key of a block cipher implemented to run on a GPU. We present a side-channel power analysis methodology to extract all of the last round key bytes of a CUDA AES (Advanced Encryption Standard) implementation run on an NVIDIA TESLA GPU. We describe how we capture power traces and evaluate the power consumption of a GPU. We then construct an appropriate power model for the GPU. We propose effective methods to sample and process the GPU power traces so that we can recover the secret key of AES. Our results show that parallel computing hardware systems such as a GPU are highly vulnerable targets to power-based side-channel attacks, and need to be hardened against side-channel threats.

In this paper, we propose a method to measure the relationship between data samples, which is dependent on the possibility whether they are within a homogeneous region or not. By considering the regional variation, this possibility is formulated in terms of the maximum local variation along the shortest path connecting the samples. The metric is concretized in both 2D images and 3D meshes, and then integrated into smoothing filters. Benefited from our method, the improved filters tend to effectively preserve the structural component of data. Moreover, our method is implemented in various applications such as image denoising, image decomposition and mesh smoothing, which demonstrates better performance in comparison to the previous work.

Pre-configured protection cycles (p-cycles) have drawn many attentions due to the fully pre-connected cyclic structures, ring-like speed, mesh-like spare capacity efficiencies and fast switching characteristics. In this paper, we propose a novel removal of loop back (RLB) approach with standard protocol extension (the flooding based distributed cycle pre-configuration (F-DCPC)) to solve the resource inefficiency issue due to long restored paths for symmetric dynamic networks. Meanwhile, distributed cycle pre-configuration (DCPC) is a representative technique for automatic p-cycle configuration. But for the mesh network topology under asymmetric service distribution, it works especially with more network convergence time and larger amount of extra controlling overhead. So in our RLB approach, F-DCPC scheme using generalized multi-protocol label switching (GMPLS) protocol to solve the problems of configurations and reconfigurations with asymmetric digraph in high-speed optical mesh networks is presented. Additionally, to describe the basic idea of traffic splitting strategy in the unidirectional resource distribution, we present the mathematic model with a heuristic algorithm to accelerate resource configuration. We evaluate the performance of F-DCPC utilizing proposed RLB approach with various classical p-cycles enumeration algorithms and allocation strategies by experimental simulations implemented in OPNET modeler, and the simulation results show the effectiveness of the proposed scheme.