Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment

With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain’s mechanisms of language processing within the context of the Chinese natural language

Wavelength-tunable L-band mode-locked fiber laser using a long-period fiber grating

We demonstrate an L-band wavelength-tunable passively mode-locked fiber laser using a single long-period fiber grating (LPFG) as a narrow-band optical attenuator (NBOA). Through bending the LPFG, the central wavelength can be continuously tuned from 1582.02 to 1597.29 nm, while the output power only varies from 1.465 to 1.057 mW, approximately a rate of 22 µW/nm variation. This is the first time that LPFG is functioned as a NBOA in mode-locked fiber lasers, showing the great advantage of less impact on output power variation reduction. Besides, the total cavity length is 5.08 m, which is the shortest length yet reported in wavelength-tunable mode-locked fiber lasers. The wavelength tuning could also be realized at harmonic mode locking with tuning range of 14.69 nm under 5th harmonic

Novel Y-chromosomal microdeletions associated with non-obstructive azoospermia uncovered by high throughput sequencing of sequence-tagged sites (STSs)

Y-chromosomal microdeletion (YCM) serves as an important genetic factor in non-obstructive azoospermia (NOA). Multiplex polymerase chain reaction (PCR) is routinely used to detect YCMs by tracing sequence-tagged sites (STSs) in the Y chromosome. Here we introduce a novel methodology in which we sequence 1,787 (post-filtering) STSs distributed across the entire male-specific Y chromosome (MSY) in parallel to uncover known and novel YCMs. We validated this approach with 766 Chinese men with NOA and 683 ethnically matched healthy individuals and detected 481 and 98 STSs that were deleted in the NOA and control group, representing a substantial portion of novel YCMs which significantly influenced the functions of spermatogenic genes. The NOA patients tended to carry more and rarer deletions that were enriched in nearby intragenic regions. Haplogroup O2* was revealed to be a protective lineage for NOA, in which the enrichment of b1/b3 deletion in haplogroup C was also observed. In summary, our work provides a new high-resolution portrait of deletions in the Y chromosome.National Key Scientific Program of China [2011CB944303]; National Nature Science Foundation of China [31271244, 31471344]; Promotion Program for Shenzhen Key Laboratory [CXB201104220045A]; Shenzhen Project of Science and Technology [JCYJ20130402113131202, JCYJ20140415162543017]SCI(E)[email protected]; [email protected]; [email protected]

Dynamic Analysis and DSP Implementation of Memristor Chaotic Systems with Multiple Forms of Hidden Attractors

In this paper, a new six dimensional memristor chaotic system is designed by combining the chaotic system with a memristor. By analyzing the phase diagram of the chaotic attractors, eleven different attractors are found, including a multi-wing attractor and symmetric attractors. By analyzing the equilibrium point of the system, it is proven that the system has the property of a hidden chaotic attractor. The dynamic behavior of the system when the three parameters change is analyzed by means of LEs and a Bifurcation diagram. Other phenomenon, such as chaos degradation, coexistence of multiple attractors and bias boosting, are also found. Finally, the simulation on the DSP platform also verifies the accuracy of the chaotic system simulation. The theoretical analysis and simulation results show that the system has rich dynamical characteristics; therefore, it is suitable for secure communication and image encryption and other fields

Wavelength-Tunable L-Band High Repetition Rate Erbium-Doped Fiber Laser Based on Dissipative Four-Wave Mixing

A wavelength-tunable high repetition rate (HRR) erbium-doped fiber laser in L-band based on dissipative four-wave mixing (DFWM) mechanism is demonstrated. The cavity can generate a single-soliton train and bound-soliton train with a fixed repetition rate of ~126 GHz, which is determined by the free spectral range of the intra-cavity Lyot filter. A wide wavelength-tuning operation can also be obtained by rotating the polarization controllers. The wavelength-tuning ranges of the HRR single-soliton state and HRR bound-soliton state are ~38.3 nm and ~22.6 nm, respectively. This laser provides useful references for the area of a wavelength-tunable fiber laser with high repetition rate. The laser may also find useful applications in high-speed communication, sensing, etc

Study on Stress Corrosion Characteristics of Drill Rod Joint Under Mechanical Effects

Thanks to the complex underground environment that coal mines enjoy, drill rods for mining are vulnerable to corrosion during operation. To investigate the impact of the corrosion defects on the residual intensity of the drill rod, a relational expression of stress versus corrosion rate in the conditions of uniform and local corrosions is deduced based on the theory on mechanochemical effects; building on this, a spherical corrosion defect is developed in the joint of a φ73 mm drill rod while it is exerted with a make-up torque, an axial force and a bending moment. So it is found that, when the corrosion defect is under pressure, the bending moment plays a certain role to inhibit its increase, however when it is under tension, the bending moment plays the role to drive its increase so as to quicken its corrosion rate while the impact of the change in the corrosion detect depth is much greater than that of the radius. The result from the research provides a basis to evaluate the residual intensity of the drill rod and theoretical basis to protect drill rods from corrosion

Green Development Assessment of a Stainless-steel Industrial Park Based on Material Flow Analysis (MFA)

Based on the material flow analysis method, the green development index system of industrial park was constructed. By using the PSR model, 13 indexes were divided into pressure layer, state layer and response layer, the indexes were standardized, weight distribution and synthesis. The green development level of industrial park was divided into 5 grades combined with the pressure state response factors. The results show that the energy consumption per unit industrial output of a stainless-steel industrial park is 0.7116 tons of standard coal per million yuan, the water consumption per unit industrial output is 6.1466 cubic meters per million yuan, the wastewater discharge per unit industrial added value is 3.12 tons per million yuan, the reduction rate of carbon emissions per unit industrial added value is 19.53%, and the green development index is 0.65, which belongs to the third level of green development. The problems are reflected in particulate emission, resource recycling, energy efficiency, industrial structure, and park greening. In the future, the stainless-steel industrial park should focus on industrial restructuring and transformation, environmental protection infrastructure construction, new policy guidance, and ecological environment construction of the park

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods