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The recognition of medical images, especially endoscopic ultrasound images, has the characteristics of changing images and insignificant gray-scale changes, which requires repeated observation and comparison by medical staff. In view of the above-mentioned characteristics of ultrasound imaging, a system scheme suitable for image processing is proposed, which can analyze the biliary tract, gallbladder, abdominal lymph nodes, liver, descending duodenum, duodenal bulb, stomach, pancreas, pancreatic lymph nodes, there are a total of 10 ultrasonic organs, including 21 kinds of sub-categories and 3510 images. The images are preprocessed using binarization, histogram equalization, median filtering and edge enhancement algorithms. The improved YoloV4 convolutional neural network algorithm is used to train the data set and perform high accuracy is detected in real time. Finally, the average accuracy of this algorithm has reached 91.59%. The algorithm proposed in this paper can make up for the shortcomings of manual detection in the original image detection system, improve the efficiency of detection, and at the same time as an auxiliary system can reduce detection misjudgments, and promote the development of automated and intelligent detection in the medical field.

Distributed Generations are usually connected to the ac grid through HVDC transmission systems. However, due to the current injected by the grid-side voltage source converter (GVSC), the voltage drop after a short-circuit fault will be partly compensated. For this reason, the viewed impedance of distance relays will be affected, which is a challenge for the distance protection scheme. For this issue, this paper proposed an adaptive distance protection scheme for the system containing distributed generations. Firstly, the proposed scheme controls the current of the GSVC to be proportional to the current of the AC transmission line. Secondly, an adaptive distance protection criterion is proposed, which can obtain the impedance from the relay to the fault-point accurately. The effectiveness of the proposed approach was validated through simulation using MATLAB/Simulink.

Accurate and stable prediction of NOx emissions from diesel vehicles plays a crucial role in the establishment of virtual NOx sensors and the development and design of diesel engines. This paper presents a method for estimating transient NOx emissions by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a long- and short-term memory neural network (LSTM). First, the CEEMDAN algorithm is used to reduce the non-stationarity and volatility of the transient NOx emission data to obtain multiple subseries with different frequencies. Secondly, a predictive model is developed for each subsequence using an LSTM neural network. Finally, the results of each subsequence prediction are summed to obtain the final prediction. The proposed model uses NOx emission data generated by an EU IV diesel bus during real road driving. The results show that (1) The use of CEEMDAN can effectively improve the smoothness of NOx transient emission data, as well as facilitate more effective extraction of internal characteristics and variations of the raw data. (2) LSTM has better learning and prediction capability for transient changes in NOx emissions. (3) The results of CEEMDAN-LSTM for RMSE, R2, MAE and NRMSE are 46.11,0.98, 29.82 and 2.71, respectively, which are better than the other model with improved prediction performance.

Yield optimization aims at finding microwave filter designs with high yield under fabrication tolerance. The electromagnetic (EM) simulation-based yield optimization methods are computationally expensive because a large number of EM simulations is required. Moreover, the microwave filter design usually requires several performance objectives to be met, which is not considered by the current yield optimization methods for microwave filters. In this paper, an efficient yield-constrained optimization using polynomial chaos surrogates (YCOPCS) is employed for microwave filters considering multiple objectives. In the YCOPCS method, the low-cost and high-accuracy of polynomial chaos is used as a surrogate. An efficient yield-constrained design framework is implemented to obtain the optimal design solution. Two numerical examples demonstrate the performance of the YCOPCS method, including a coupling matrix model of a fourth-order filter with cascaded quadruplet topology and an EM simulation model of a microwave waveguide bandpass filter. The numerical results show that the YCOPCS method can obtain the filter designs with higher yield and reduce EM simulations by 80% compared to Monte Carlo-based yield optimization in all testing examples.

Topic Detection and Tracking technique (TDT) has been commonly used to identify the hot topics from the huge volume of Internet news information and keep up with the hot news. However, traditional topic detection and tracking methods have shown low accuracy and low efficiency. In this paper, a topic detection system driven by big data is built on the Spark platform, which aims at improving the efficiency of news collecting from the Internet and improving the accuracy and efficiency of topic detection and tracking tasks. This system can be easily employed in a distributed architecture and work as a parallelized news collecting and topic detection system. An improved density-based spatial clustering of application with noise (DBSCAN) clustering algorithm based on the time window is proposed to achieve accurate topic detection with the auxiliary advantage of reducing the time complexity. A parallel KNN based topic tracking algorithm is proposed for the topic tracking task. Experiments including comparison with some baseline algorithms and quantitative and qualitative analyses are conducted on pseudo-distributed Spark platform, which demonstrates the effectiveness and efficiency of the parallelized topic detection system.

The segmentation and modeling of the upper airway play an important role in the analysis and diagnosis of obstructive sleep apnea. In this paper, a method based on deep learning is proposed to automatically segment the upper airway on CBCT data. The main process is to obtain a coarse segmentation result map through 3D U-Net. Then low-confidence voxels in the coarse segmentation results as weak feature regions are extracted. Secondly, the boundary attention map is constructed through the Gaussian kernel function based on weak feature regions. Then, the boundary attention mechanism is used to strengthen the learning ability of weak feature regions. Finally, a high-precision 3D model is constructed on the predicted segmentation results through the marching cube algorithm. A large number of experiments and comparisons verify that our method achieves better performance on the manually annotated dataset.

Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems. However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further development in this field. In this paper, a novel monitoring-forecasting system, including the analysis module, the outlier detection module, the probabilistic forecasting module, and the evaluation module, is proposed for uncertainty modeling of wind power. In the analysis module, recurrence analysis techniques are developed, aiming at characterizing complicated patterns of wind power. Furthermore, the interval partitioning-based isolation forest algorithm, which can effectively address the effects of swamping and masking, is first developed in the outlier detection module for wind power. Superior to the traditional point forecasting method that cannot perform quantitative characterization of the intrinsic uncertainties in wind power forecasting, an advanced probabilistic forecasting method based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating that the forecast skill of GPR is significantly enhanced. Finally, the proposed system is validated using real wind power data with high resolution from Spain in the evaluation module, solidly demonstrating its high reliability and flexibility compared to benchmarks considered in this study.

At present, the primary technical deterrent to the use of strawberry harvesting robots is the low harvest rate, and there is a need to improve the accuracy and real-time performance of the localization algorithms to detect the picking point on the strawberry stem. The pose estimation of the fruit target (the direction of the fruit axis) can improve the accuracy of the localization algorithm. This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points. First, the lightweight network Mobilenet-V1 was used to replace the convolution neural network as the backbone network for feature extraction. The simplified network structure substantially increased the operating speed. Second, the rotation angle parameter $\alpha $ was used to label the training set and set the anchors; the rotation of the bounding boxes of the target fruits was predicted using logistic regression with the rotated anchors. The test results of a set of 100 strawberry images showed that the proposed model's average recognition rate to be 94.43% and the recall rate to be 93.46%. Eighteen frames per second (FPS) were processed on the embedded controller of the robot, demonstrating good real-time performance. Compared with several other target detection methods used for the fruit harvesting robots, the proposed model exhibited better performance in terms of real-time detection and localization accuracy of the picking points. Field test results showed that the harvesting success rate reached 84.35% in modified situations. The results of this study provide technical support for improving the target detection of the embedded controller of harvesting robots.

Dendritic neuron model (DNM) that utilizes a single dendritic neuron to emulate the information processing in human brains has been successfully applied to classification, approximation, and prediction fields. However, it is still a challenging task to improve the performance for forecasting the time series with outliers. A time series is a collection of data ordered in time. An outlier in time series as an input can affect prediction performance. Thus, in this study, we first propose a novel median dendritic neuron model (MDNM) for prediction to deal with time series with outliers. In MDNM, DNM and median function are combined to achieve better forecasting results. Moreover, states of matter search (SMS) algorithm is used to train MDNM. Twenty real time series and a benchmark time series are adopted to verify the performance of MDNM. The comparison between the MDNM and DNM suggests that MDNM is effective for forecasting time series with outliers.

Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

In business process collaboration (BPC), especially when it comes to message communication and data exchange, there are complex data dependencies among sender process, receiver process and messages. However, each participant of the overall BPC develops its part independently as a service, including its own communication part and data flow. As a result, data flow errors across processes may occur easily. In this article, we propose a method based on BPMN to detect these errors caused by data dependency violations. Our method is inspired by the study of detecting data flow errors within a single process and focuses on a subset of the elements of the BPC model, without having to consider the complete set. In particular, we define a set of data flow error patterns by analyzing and formalizing data dependencies in order to clearly clarify and identify errors. Then we give the corresponding automatic detection algorithm. Finally, through two evaluations, we demonstrate the effectiveness of our proposal.

Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters. However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster. To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). First, we redefine the local density calculation method using the layered k-nearest neighbors. To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers. Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton's law of gravitation, and a subcluster merging strategy is proposed. To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets. A large number of experiments demonstrate that our algorithm can often outperform other algorithms.

Key frame extraction technology is one of the core technologies of content-based video retrieval. For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal. Based on the Visual Geometry Group (VGG), this article proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features. The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed. A new method for extracting key frames of motion video is introduced in detail. Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images. The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target. Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing. The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing.

The Solid (Social Linked Data) project focuses on data sharing and privacy security and aims to build a decentralized ecosystem that radically changes the way web applications work today. Our goal is to introduce a “trust access authentication system” to achieve secure authentication and fine-grained access control, thereby promoting the implementation of Solid. Blockchain, equipped with multiple security properties and authentication functions, is a crucial technology. In this paper, we present a blockchain-assisted system for secure authentication in Solid and for implementation of fine-grained access control policies. Specifically, we explore to integrate threshold RSA signatures in a permissioned blockchain system to enable a fault-tolerant distributed signature scheme, thereby enhancing the resilience and robustness of authentication system. Moreover, we utilize smart contract to control transaction flows and manage access control policies automatically. Experimental results show that our proposed trust access authentication system enhances security, scales well, and is efficient and economically feasible.

There is a very important index in the influence of human visual perception, that is, the degree of the motion blur about the moving image on display device. In this paper, a new kind of improved method is proposed to calculate the response time of liquid crystal under the new backlight driving and modulation, which is more accurate than the traditional moving window integration method. This paper also discusses the eye tracking movement of the human visual system, including studying the influence of image content, size, motion speed and motion direction, based on the human vision property study and the analyzes of the simulation model of image motion artifacts perceived by human eyes. Experiments are performed, using the human eye vision perception measurement system. The experiment data shows that the edge blur time can be reduced to 10.5 ms, compared with the 16.5 ms in traditional drive, reduced by 36.4%, and the human perception of motion blur can be markedly reduced. Also, the results show that the simulation results are in good agreement with the actual measurement data by modifying the proportion coefficient of the eye tracking object model to 0.9. At the same time, through the improved human eye tracking model of smooth moving objects, the actual motion blur phenomenon perceived by the human eye can be calculated more accurately, and the results can be used in image processing algorithms to improve the quality of the moving image on the display.

This paper addresses the trajectory tracking problem for constrained high dynamic electro-hydraulic actuator in the presence of time-varying parameters, high frequency external load interference, measurement noise and some unmeasurable states. An adaptive robust optimal control scheme is proposed for the electro-hydraulic actuator in legged robot. The framework of our presented scheme is based on a linear time-varying model predictive controller (LTV-MPC) embedded with a multi-scale online estimator (MEKF). With fast- varying and slow- varying time scales, the MEKF part is used not only for measurable states filtering and unmeasurable states estimation, but also for time-varying parameters and external load interference estimation, which will be integrated into the mpc model in real time. The LTV-MPC part is a trajectory tracking controller designed by constrained MPC with an approximate high-precision real-time model and a rapidly solved cost function, which guarantees that the input and output constraints are satisfied during the receding horizon and optimal control process. Finally, with a series of highly dynamic conditions, the comparison experiment results show that the proposed controller has a simple design process, strong adaptive robust performance and trajectory tracking performance, which verifies the effectiveness of the control scheme.

Recent researches on hypersonic vehicles adopted in military have gained a lot of interest because of its high flight speed and efficiency. However the guidance laws for hypersonic missiles against the anti-missile system to increase survivability and defense penetration ability are still need to study. One main countermeasure to deal with this issue is to implement simultaneous attack. Therefore in this paper an impact-time-control guidance law for hypersonic missiles to impact a stationary target at the same desired final time is presented. Time-to-go estimation for time-varying velocity is derived by using a method of approximate acceleration form to extend the estimation method for constant velocity. The impact-time-control guidance law is firstly given and applied in vertical plane based on proportional navigation guidance law to control the impact time in terminal phase. Then the conditions that limit the method to apply are discussed. To overcome this issue, the guidance law applied in lateral plane is derived. The simulations are implemented using each method in vertical and lateral planes respectively and show good results for hypersonic missiles to achieve simultaneous attack.

Node localization in wireless sensor networks (WSNs) has received a considerable amount of attention. In this paper, using the natural low-rank properties of the Euclidean distance matrix (EDM), we formulate the node location finding problem from only a small fraction of random entries of the EDM as a low-rank matrix recovery problem. A Bayesian-learning-based method is utilized to recover the original EDM, based on which the relative positions of all the sensor nodes in WSNs are accurately estimated by applying classical multi-dimensional scaling (MDS). In addition, with the location knowledge of anchor nodes, we transform the relative positions into absolute positions. The simulation results illustrate that our proposed approach leads to superior performance over various other methods.

Due to the development of intelligent electric devices and advanced metering infrastructures, demand response will be widely utilized in the trading of the electricity market and maintain energy balance of the power system. In this paper, a two-stage nested bilevel model for the optimal bidding strategy of a load agent (LA) with incentive-based demand response in day-ahead and balancing markets is proposed. In the upper-level model, the optimal trading strategy of the LA is formulated to maximize the operating profit of the LA in the day-ahead energy and balancing markets. On the other hand, the lower-level proposes the clearing market model of the independent system operator, which aim to maximize social welfare. The LA acts as a price-maker in the first-stage of the bilevel model, which is bilevel nonlinear programming (BNLP) problem. Karush-Kuhn-Tucker conditions and dual theory are used to transform the BNLP into single-level programming. The LA acts as a price-taker accepts the day-ahead energy clearing-price in the second-stage of the bilevel model, which is a bilevel mixed-integer linear stochastic programming problem. Finally, implementing the two-stage nested bilevel model on modifying the 8-bus power system demonstrates the applicability of the proposed model and analysis the sensitivity of the LA' profit to unit price and the committed reserve capacity demand of the day-ahead reserve market.

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.

Micro-expression recognition has been an active research area in recent years, it plays an important role in psychology and public security. Due to the aspects of short duration and subtle movement, it is challenging to extract spatiotemporal features of micro-expressions. The existing methods only extract features in the three-dimensional orthogonal plane and fail to make full use of that information. To solve this problem, we propose a new Local Cubes Binary Patterns (LCBP) method for micro-expression recognition. LCBP is cascaded by the motion information LCBPdirection, the amplitude information LCBPamplitudes, and the spatial information LCBP3D to obtain the spatiotemporal features. The advantage of LCBP is its ability to preserve the spatiotemporal information and the low feature dimension. Furthermore, to increase the discrimination of features in micro-expression sequences, we apply a differential calculation energy map to find regions of interest (ROI) for getting a weighted energy map. The final micro-expression feature acquired by fusing the LCBP features and the weighted energy map are classified through the Support Vector Machine (SVM). We evaluate the proposed method on four published micro-expression databases including SMIC, CASME, CASME2, SAMM. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.

Seru production can be used to enhance productivity, such as makespan reduction and manpower saving. Seru system operation includes two NP-hard problems, i.e., seru formation and seru scheduling. The exact solution cannot be obtained by solving only one of seru formation and seru scheduling. We develop a cooperative coevolution algorithm for the Seru production with minimizing makespan by solving the seru formation and seru scheduling simultaneously. The cooperative coevolution algorithm includes two evolution algorithms, i.e., the algorithm combining generic algorithm and local search, and the ant colony optimization algorithm. The former algorithm is used to deal with the evolution of seru formation. The latter is used to find a better seru scheduling. In the cooperative mechanism, the two algorithms cooperate to seek a better solution of seru system operation. Finally, extensive-tested experiments show that the proposed cooperative coevolution algorithm can obtain a better solution than all the existing algorithms and, even, can obtain the exact solution for some medium-and-small instances.

Feature fusion is widely used in person re-identification (re-ID) and has been proven effective. However, it is difficult to know which features are effective to identify a specific person and how to fuse features to explore complementary information and apply the advantages of each feature. Motivated by these problems, this paper proposes a new method of person re-ID to fuse the recognition results of multiple features at the rank level. Three innovations are included in this method: first, multiple metric spaces are constructed based on the correlation of different features to generate multiple rank results; second, the most similar candidates in each corresponding rank list is converted into a graph structure by our proposed weighted center graph (WCG), and we use an adaptive value $K$ to automatically seek the most similar images of each query, thus improving the accuracy of candidate targets. Finally, to evaluate the effect of each WCG, a discriminative power coefficient is designed and used to assign a proper coefficient for each WCG according to the discriminative power of corresponding features. The result can be obtained by re-ranking the fused WCG. The extensive experiments on five datasets demonstrate the matching rate of our proposed method by comparing with several state-of-the-art methods. Our code is available at https://github.com/gengshuze/WCG.git .

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers’ traveling time.

In this paper, a third-order waveguide bandpass filter (BPF) based on slotted spherical resonators with a wide spurious-free stopband is presented. The resonator consists of a spherical cavity with slots opened at the top and bottom. Compared with a non-slotted spherical resonator, the slotted resonator suppresses the two spurious modes (TM211 and TE101) whilst maintaining the fundamental TM101 mode. The unloaded quality factor of the TM101 mode is not significantly degraded. This is achieved by interrupting surface current and radiating the unwanted spurious modes with the slots. The BPF is designed at a center frequency of 10 GHz with a fractional bandwidth of 1%. Two filter prototypes are fabricated, one using metal-based selective laser melting (SLM) and the other by polymer-based stereolithography apparatus (SLA) techniques. The slots also facilitate the copper electroplating process for the SLA-printed filter. The measured results show that the average passband insertion losses of the SLM- and SLA-printed filters are 0.33 and 0.2 dB, respectively. The corresponding passband return losses are better than 22 and 20 dB. The filters demonstrate excellent passband performance and wide spurious-free stopbands up to 16 GHz with stopband rejections of over 20 dB.

With the development of the mobile Internet, novel things are constantly emerging, and among them, online car-hailing is one of the representatives. However, the rapid development of online car-hailing also brings about normative problems and safety loophole, the most prominent of which is the inconsistency between operating cars and registered drivers. Some existing algorithms based on curve similarity measurements, such as Hausdorff distance and discrete Fréchet distance, can be used to solve this problem. However, they only consider one of the characteristics of the two curves, such as the distance or the area between two curves, which does not achieve good results on this problem. On this ground, we propose a model based on curve comparison, named multi-feature fusion (MFF), which extracts the features of length, distance, and area from the GPS positioning track of car and the application mobile phone of the driver, to testify whether the car is being operated by the registered driver and thus solving the problem of mismatch between the driver and the car during the operation. Among them, the MFF model designs different algorithms for different features and fuses different features by an ensemble learning method. The experimental results prove that the model can effectively detect the mismatch between drivers and cars.

Seru production can be used to enhance productivity, such as makespan reduction and manpower saving. Seru system operation includes two NP-hard problems, i.e., seru formation and seru scheduling. The exact solution cannot be obtained by solving only one of seru formation and seru scheduling. We develop a cooperative coevolution algorithm for the Seru production with minimizing makespan by solving the seru formation and seru scheduling simultaneously. The cooperative coevolution algorithm includes two evolution algorithms, i.e., the algorithm combining generic algorithm and local search, and the ant colony optimization algorithm. The former algorithm is used to deal with the evolution of seru formation. The latter is used to find a better seru scheduling. In the cooperative mechanism, the two algorithms cooperate to seek a better solution of seru system operation. Finally, extensive-tested experiments show that the proposed cooperative coevolution algorithm can obtain a better solution than all the existing algorithms and, even, can obtain the exact solution for some medium-and-small instances.

Intrusion detection has been an important countermeasure to secure computing infrastructures from malicious attacks. To improve detection performance and reduce bias towards frequent attacks, this paper proposes a two-step hybrid method based on binary classification and $k$ -NN technique. Step 1 employs several binary classifiers and one aggregation module to effectively detect the exact classes of network connections. After step 1, the connections whose classes are uncertain are sent to step 2 to further determine their classes by the $k$ -NN algorithm. Step 2 is based on the outcomes of step 1 and yields a beneficial supplement to step 1. By combining the two steps, the proposed method achieves reliable results on the NSL-KDD data set. The effectiveness of the proposed method is evaluated in comparison with five supervised learning techniques. Experimental results demonstrate that the proposed method outperforms baselines with respect to various evaluation criteria. In particular, for U2R and R2L attacks, the F1-scores of the proposed method are much higher than those of baselines. Furthermore, comparisons with some recent hybrid approaches are also listed. The results illustrate that the proposed method is competitive.

A novel method of generating multi-frequency test stimuli for incipient faults is presented to improve the fault detection accuracy of analog circuits. This paper analyzes the primary cause of low incipient fault detection accuracy and indicates that the high aliasing between the incipient fault response and the normal response seriously impairs the fault recognition ability of fault classifiers. Therefore, this paper builds an aliasing measuring model (AMM) to generate the multi-frequency test stimuli set for incipient faults. The principle part of the AMM is the aliasing measuring algorithm (AMA), which uses the response aliasing as the pivotal index to evaluate the test frequency. The test frequencies with smaller response aliasing will be selected. The other part of the AMM contains the genetic algorithm and the greedy algorithm, which can advance the AMA to quickly generate the multi-frequency test stimuli set for the incipient fault and can ensure that the obtained test set covers the entire circuit and contains fewer test frequencies. The simulation experimental results validate two conclusions: the test frequencies obtained by the AMM remarkably increase the incipient fault detection accuracy and reduce the time for test frequency generation and the multi-frequency test stimuli set contains fewer elements. The hardware experimental results demonstrate that the proposed method is practicable and effective.

Zero injection (ZI) is an inherent characteristic of a power grid. The effect of ZI (ZI effect) is usually utilized as an auxiliary measurement termed ZI observation for simplification. We discover that the ZI observation can transmit. The reliability of ZI observation becomes weaker, as ZI observation transmits “deeper.” In this paper, a unified phasor measurement unit (PMU) placement model is proposed with considering the reliability of ZI observation. To improve the reliability of ZI observation, the depth of ZI observation is limited, and the ZI utilization rate is minimized. The optimality problem which considers reliable ZI observation is studied, which has not been discussed before. Five working conditions, i.e., base case, $N-1$ PMU outage, $N-1$ line outage, $N-1$ PMU outage, or $N-1$ line outage, and the PMU measurement channel limitation are studied. The proposed method is tested on six IEEE test systems, i.e., IEEE 14-, 30-, 39-, 57-, 118-, and 300-bus systems, the Polish 2383-bus test system, and a practical power system in China. Results indicate that the proposed method not only reduces the number of PMUs by employing ZI effect but also ensures the reliability of ZI observation while making full observation of the power system.

With the development of smart grid and active distribution network, the flexible load recourse would play a key role in the electricity market. In this paper, we proposed a framework that the distributed storage energy systems, electric vehicles, and temperature control loads are aggregated in the flexible load aggregator, trading in day-ahead energy and reserve markets. The framework is modeled as a bilevel optimization model. In the propose model, the operation problem of the FLA is modeled in upper-level problem, which is to maximize the profit of the aggregator. The biding and offering strategic of Gencos and flexible load aggregator in the independent system operator are presented in lower-level problem, which aim at improving the social benefits. Karush–Kuhn–Tucker and dual theory are used to transform the nonlinear bilevel problem to a mixed-integer linear programming of single-level model. Finally, the numerical studies based on modifying PJM-5bus power system, showing the effectiveness of the proposed framework and bilevel model.

In this paper, we present a TSV low-bandwidth equivalent lumped circuit model of common TSV defects as analyzed using a high-frequency structure simulator 3-D full wave simulation. By using the proposed model, the physical parameters of TSV are related to its electrical parameters, providing the possibility for defect diagnosis. The simulation results show that only some of the common defects tend to vary the electrical parameters of TSVs. Based on these conclusions, we present a novel TSV-defect detection and diagnosis method using switched-capacitor circuits. The proposed method can effectively detect significant void defects, pinhole defects, and missing micro-bump defects using parametric measurement and has been verified through HSPICE simulation.