A long-distance quantum key distribution scheme based on pre-detection of optical pulse with auxiliary state

We construct a circuit based on PBS and CNOT gates, which can be used to determine whether the input pulse is empty or not according to the detection result of the auxiliary state, while the input state will not be changed. The circuit can be treated as a pre-detection device. Equipping the pre-detection device in the front of the receiver of the quantum key distribution （QKD） can reduce the influence of the dark count of the detector, hence increasing the secure communication distance significantly. Simulation results show that the secure communication distance can reach 516 km and 479 km for QKD with perfect single photon source and decoy-state QKD with weak coherent photon source, respectively.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

Improved Relative-transformation Principal Component Analysis Based on Mahalanobis Distance and Its Application for Fault Detection

主要部件分析(PCA ) 广泛地在过程工业被使用了，它能维持最大的差错察觉率。尽管许多问题在 PCA 被处理了，一些必要问题仍然保持未解决。这研究以下列方法为差错察觉性能改进 PCA。第一，一个相对转变计划基于 Mahalanobis 距离(MD ) 被介绍消除数据的尺寸的效果而不是无尺寸的标准化，并且改进精确性和差错察觉的即时性能。理论推导证明那相对转变能直接基于 MD 消除尺寸的效果并且在结果显示出的相对空间，分析和模拟给 PCA 的合理解释它的优势和有效性。第二，一个改进摆平的预言错误(SPE ) 统计数值被给改进标准化 PCA 的差错察觉表演，它能使标准化基于 PCA 的差错察觉方法成为对实际工业过程合适的更多。最后，二个改进方法被联合更有效地检测差错。建议方法被使用在热连续滚动过程检测 looper 系统的单个差错和多差错，模拟结果以易感知，精确性和差错察觉的即时性能为差错察觉性能表明这些改进的有效性。

Dynamical Mechanisms of Effects of Landslides on Long Distance Oil and Gas Pipelines

According to the investigations on the oil and gas pipelines such as the Lan-Cheng-Chong pipeline and the Southwest pipeline, there are two ways of laying pipeline： pipelines paralleling （approximately） to the main slide direction and pipelines perpendicular （approximately） to the main slide direction. If earth-retaining walls have been built for pipelines paralleling to the main slide direction, they will prevent the lands from sliding; On the contrary, without earth-retaining walls, the sharp broken rocks in the backfilling soil will scratch the safeguard of the pipeline when the landslides take place. Pipelines perpendicular to the main slide direction can be classified into four types according to the relative positions between pipelines and landslides： Pipelines over the slide planes, pipelines inside the fracture strips of slide planes, pipelines below the slide planes and pipelines behind the backsides of landslides. The different dynamical mechanisms of the process in which landslide acts against pipelines are analyzed based on whether the pipelines are equipped with fixed frusta, because the sliding resistance depends on whether and how many fixed frusta are equipped and the distance between frusta.

Fault detection observer design for networked control system with long time-delays and data packet dropout

Focusing on the networked control system with long time-delays and data packet dropout,the problem of observerbased fault detection of the system is studied.According to conditions of data arrival of the controller,the state observers of the system are designed to detect faults when they occur in the system.When the system is normal,the observers system is modeled as an uncertain switched system.Based on the model,stability condition of the whole system is given.When conditions are satisfied,the system is asymptotically stable.When a fault occurs,the observers residual can change rapidly to detect the fault.A numerical example shows the effectiveness of the proposed method.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.

High Sensitive Long Period Fiber Grating Sensor for Detection of Nitrite

A high sensitive long period fiber grating（LPFG） sensor for the detection of nitrite is proposed, which is realized by coating multiple poly（sodium 4-styrensulfonate）（PSS） and poly（diallyldimethylammonium） chloride （PDDA） layers on the fiber grating surface. The sensitivity of this LPFG sensor is maximum when the number of assembled layers is 70. Under this condition, a nitrite concentration of 3×10^-3 mol/L, which is lower than the National Food Additive Standard, 4.2×10^-3 mol/L, can be distinguished. The sensitivity is further increased by 30% when nitrite was determined in the sucrose solution with a concentration of 65%, which provides a new solution for the best refraction index approaching matched index of the fiber cladding. Compared with chemical methods, this nitrite detection technology offers some advantages, such as high accuracy, non toxicity, high speed, low cost, without chemical reagent, and is suitable for foodstuff security detection.

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Pressure-relief effect of coal rock body of long distance lower protective seam mined based on FLAC^(3D)

According to the specific geological condition, analyzed the stress distribution of the overlying strata, the displacement of pressure released seam, thickness variation and the distribution of plastic zones by FLAG3D software to simulate mining of the long-distance lower protective seam. The research results show that the distribution of vertical stress appears as a ＂Double-hump＂ within the pressure-relief range of the protected coal seam and the swelling deformation curve of coal bodies takes an ＂M＂ shape. The swelling is divided into initial swelling, swelling increase and swelling compression stability. The maximum swelling ratio of the pressure released seam is 1.84%, protection angle of the lower protective coal seam along the strike direction is about 55°, protection angle below the dip direction is about 50°, protection angle above the dip direction is about 55°, and the coal seam compression zone resembles a ＂U＂ shape.

Study on the long-distance target apperception techniques for underwater vehicles

The limited physical size for autonomous underwater vehicles （AUV） or unmanned underwater vehicles （UUV） makes it difficult to acquire enough space gain for localizing long-distance targets. A new technique about long-distance target apperception with passive synthetic aperture array for underwater vehicles is presented. First, a synthetic aperture-processing algorithm based on the FFT transform in the beam space （BSSAP） is introduced. Then, the study on the flank array passive long-distance apperception techniques in the frequency scope of 11-18 kHz is implemented from the view of improving array gains, detection probability and augmenting detected range under a certain sea environment. The results show that the BSSAP algorithm can extend the aperture effectively and improve detection probability. Because of the augment of the transmission loss, the detected range has the trend of decline with the increase of frequency under the same target source level. The synthesized array could improve the space gain by nearly 7 dB and SNR is increased by about 5 dB. The detected range is enhanced to nearly 2 km under the condition of 108-118 dB of the target source level for AUV system in measurement interval of nearly 1 s.

Study of Distortion of a Vacuum Arc at a Long-Gap Distance

Experimental study of the long-gap distance vacuum arc distortion for three types of axial magnetic field （AMF） contacts, by using high-speed charge coupled device （CCD）, is presented. The arc current was of a half-cycle sine wave with a frequency of 50 Hz, produced by an L=C discharging circuit. The time of appearance and duration of vacuum arc distortion under three conditions were studied. It was found that the gap distance, current and diameter of the electrode affected the characteristics of vacuum distortion at a long-gap distance. Some characteristics of the vacuum arc at a long-gap distance were revealed and the experience and data for further investigation were provided.

Study on the Technology of Supplying Water Safely by Long-Distance Pipeline

The extensively built long-distance water transmission pipelines have become the main water sources for urban areas. To ensure the reliability and safety of the water supply, from the viewpoint of overall management, it would be necessary to establish a system of information management for the pipeline. The monitoring, calculating and analyzing functions of the system serve to give controlling instructions and safe operating rules to the automatic equipment and technician, making sure the resistance coefficient distribution along the pipeline is reasonable; the hydraulic state transition is smooth when operating conditions change or water supply accidents occur, avoiding the damage of water hammer. This paper covered the composition structures of the information management system of long-distance water transmission pipelines and the functions of the subsystems, and finally elaborated on the approaches and steps of building a mathematics model for the analysis of dynamic hydraulic status.

Scheme of long distance power supply for electrified railway traction network based on traction cable

Purpose–The traction cable is paralleled with the existing traction network of electrified railway through transverse connecting line to form the scheme of long distance power supply for the traction network.This paper aims to study the scheme composition and power supply distance(PSD)of the scheme.Design/methodology/approach–Based on the structure of parallel traction network(referred to as“cable traction network(CTN)”),the power supply modes(PSMs)are divided into cableþdirect PSM and cableþautotransformer(AT)PSM(including Japanese mode,French mode and new mode).Taking cableþJapanese AT PSM as an example,the scheme of long distance power supply for CTN under the PSMs of co-phase and out-of-phase power supply are designed.On the basis of establishing the equivalent circuit model and the chain circuit model of CTN,taking the train working voltage as the constraint condition,and based on the power flow calculation of multiple train loads,the calculation formula and process for determining the PSD of CTN are given.The impedance and PSD of CTN under the cableþAT PSM are simulated and analyzed,and a certain line is taken as an example to compare the scheme design.Findings–Results show that the equivalent impedance of CTN under the cableþAT PSM is smaller,and the PSD is about 2.5 times of that under the AT PSM,which can effectively increase the PSD and the flexibility of external power supply location.Originality/value–The research content can effectively improve the PSD of traction power supply system and has important reference value for the engineering application of the scheme.

Long-distance propagation of pseudo-partially coherent Gaussian Schell-model beams in atmospheric turbulence

Propagation properties of spatially pseudo-partially coherent Gaussian Schell-model beams through the atmo- spheric turbulence over a long-distance uplink path are studied by numerical simulation. A linear coordination trans- formation is introduced to overcome the window effect and the loss-of-resolution problem. The beam spreading, beam wandering, and intensity scintillation as functions of turbulence strength, source correlation length, and change fre- quency of random phase that models the partial coherence of the source are analyzed. It is found that the beam spreading and the intensity scintillation of the partially coherent beam are less affected by the turbulence than those of the coherent one, but it suffers from a more severe diffractive effect, and the change frequency of random phase has no evident influence on it. The beam wandering is insensitive to the variation of source correlation length, and decreases firstly then goes to a fixed value as the change frequency increases.

The Role that φ-Features Play in Long Distance Binding

Chinese long distance binding is explored in terms of a feature orientated approach: a long distance anaphor with barren or impoverished φ features is obliged to acquires φ features (or phi features) from its adjacent NPs in its upward movement at LF, and that this feature obtaining process, governed by rules that are summarized in terms of Feature Saturation Process (FSP), provides answers to long distance binding. Accordingly, binding is seen as an instance of a perfect match of features possessed by a saturated anaphor and an NP at LF. An anaphor is bound when it moves to INFL with its φ features matched with a feature functioning NP, whereas a middle way anaphor with unsaturated φ features is not bound. This approach satisfactorily explains the binding relations in sentences of long distance coreference and providing alternative answers to other issues of binding. It is further shown that the binding of Chinese reflexive ziji (自己)to its antecedent(s) results from a sequence of local dependency through movement.

Improvement of heat-tolerance by training I. Evaluation of effects of long-distance running and march training on heat-tolerance

in order to verify the heat-tolerance effect, two trainings, 90 min marching with load (WBGT 24. 6～35.6℃) and 10 km running (WBGT 25.0～31.1℃) were performed in laboratory and field under hot climate.Ten to twelve times (days) of training were carried out