Structural Engineering of Anode Materials for Low-Temperature Lithium-Ion Batteries:Mechanisms,Strategies,and Prospects

The severe degradation of electrochemical performance for lithium-ion batteries(LIBs)at low temperatures poses a significant challenge to their practical applications.Consequently,extensive efforts have been contributed to explore novel anode materials with high electronic conductivity and rapid Li^(+)diffusion kinetics for achieving favorable low-temperature performance of LIBs.Herein,we try to review the recent reports on the synthesis and characterizations of low-temperature anode materials.First,we summarize the underlying mechanisms responsible for the performance degradation of anode materials at subzero temperatures.Second,detailed discussions concerning the key pathways(boosting electronic conductivity,enhancing Li^(+)diffusion kinetics,and inhibiting lithium dendrite)for improving the low-temperature performance of anode materials are presented.Third,several commonly used low-temperature anode materials are briefly introduced.Fourth,recent progress in the engineering of these low-temperature anode materials is summarized in terms of structural design,morphology control,surface&interface modifications,and multiphase materials.Finally,the challenges that remain to be solved in the field of low-temperature anode materials are discussed.This review was organized to offer valuable insights and guidance for next-generation LIBs with excellent low-temperature electrochemical performance.

Low-temperature characteristicsof rubbers and performance testsof type 120 emergencyvalve diaphragms

Purpose–The type 120 emergency valve is an essential braking component of railway freight trains,butcorresponding diaphragms consisting of natural rubber(NR)and chloroprene rubber(CR)exhibit insufficientaging resistance and low-temperature resistance,respectively.In order to develop type 120 emergency valverubber diaphragms with long-life and high-performance,low-temperatureresistant CR and NR were processed.Design/methodology/approach–The physical properties of the low-temperature-resistant CR and NRwere tested by low-temperature stretching,dynamic mechanical analysis,differential scanning calorimetryand thermogravimetric analysis.Single-valve and single-vehicle tests of type 120 emergency valves werecarried out for emergency diaphragms consisting of NR and CR.Findings–The low-temperature-resistant CR and NR exhibited excellent physical properties.The elasticityand low-temperature resistance of NR were superior to those of CR,whereas the mechanical properties of thetwo rubbers were similar in the temperature range of 0℃–150℃.The NR and CR emergency diaphragms metthe requirements of the single-valve test.In the low-temperature single-vehicle test,only the low-temperaturesensitivity test of the NR emergency diaphragm met the requirements.Originality/value–The innovation of this study is that it provides valuable data and experience for futuredevelopment of type 120 valve rubber diaphragms.

Boosting oxygen reduction activity and CO_(2) resistance on bismuth ferrite-based perovskite cathode for low-temperature solid oxide fuel cells below 600℃

Developing efficient and stable cathodes for low-temperature solid oxide fuel cells(LT-SOFCs) is of great importance for the practical commercialization.Herein,we propose a series of Sm-modified Bi_(0.7-x)Sm_xSr_(0.3)FeO_(3-δ) perovskites as highly-active catalysts for LT-SOFCs.Sm doping can significantly enhance the electrocata lytic activity and chemical stability of cathode.At 600℃,Bi_(0.675)Sm_(0.025)Sr_(0.3)FeO_(3-δ)(BSSF25) cathode has been found to be the optimum composition with a polarization resistance of 0.098 Ω cm^2,which is only around 22.8% of Bi_(0.7)Sr_(0.3)FeO_(3-δ)(BSF).A full cell utilizing BSSF25 displays an exceptional output density of 790 mW cm^(-2),which can operate continuously over100 h without obvious degradation.The remarkable electrochemical performance observed can be attributed to the improved O_(2) transport kinetics,superior surface oxygen adsorption capacity,as well as O_(2)p band centers in close proximity to the Fermi level.Moreover,larger average bonding energy(ABE) and the presence of highly acidic Bi,Sm,and Fe ions restrict the adsorption of CO_(2) on the cathode surface,resulting in excellent CO_(2) resistivity.This work provides valuable guidance for systematic design of efficient and durable catalysts for LT-SOFCs.

Numerical parametric study on the influence of location and inclination of large-scale asperities on the shear strength of concreterock interfaces of small buttress dams

When assessing the sliding stability of a concrete dam,the influence of large-scale asperities in the sliding plane is often ignored due to limitations of the analytical rigid body assessment methods provided by current dam assessment guidelines.However,these asperities can potentially improve the load capacity of a concrete dam in terms of sliding stability.Although their influence in a sliding plane has been thoroughly studied for direct shear,their influence under eccentric loading,as in the case of dams,is unknown.This paper presents the results of a parametric study that used finite element analysis(FEA)to investigate the influence of large-scale asperities on the load capacity of small buttress dams.By varying the inclination and location of an asperity located in the concrete-rock interface along with the strength of the rock foundation material,transitions between different failure modes and correlations between the load capacity and the varied parameters were observed.The results indicated that the inclination of the asperity had a significant impact on the failure mode.When the inclinationwas 30and greater,interlocking occurred between the dam and foundation and the governing failure modes were either rupture of the dam body or asperity.When the asperity inclination was significant enough to provide interlocking,the load capacity of the dam was impacted by the strength of the rock in the foundation through influencing the load capacity of the asperity.The location of the asperity along the concrete-rock interface did not affect the failure mode,except for when the asperity was located at the toe of the dam,but had an influence on the load capacity when the failure occurred by rupture of the buttress or by sliding.By accounting for a single large-scale asperity in the concrete-rock interface of the analysed dam,a horizontal load capacity increase of 30%e160%was obtained,depending on the inclination and location of the asperity and the strength of the foundation material.

Advances in sodium-ion batteries at low-temperature: Challenges and strategies

With the continuing boost in the demand for energy storage,there is an increasing requirement for batteries to be capable of operation in extreme environmental conditions.Sodium-ion batteries(SIBs) have emerged as a highly promising energy storage solution due to their promising performance over a wide range of temperatures and the abundance of sodium resources in the earth's crust.Compared to lithiumion batteries(LIBs),although sodium ions possess a larger ionic radius,they are more easily desolvated than lithium ions.Fu rthermore,SIBs have a smaller Stokes radius than lithium ions,resulting in improved sodium-ion mobility in the electrolyte.Nevertheless,SIBs demonstrate a significant decrease in performance at low temperatures(LT),which constrains their operation in harsh weather conditions.Despite the increasing interest in SIBs,there is a notable scarcity of research focusing specifically on their mechanism under LT conditions.This review explores recent research that considers the thermal tolerance of SIBs from an inner chemistry process perspective,spanning a wide temperature spectrum(-70 to100℃),particularly at LT conditions.In addition,the enhancement of electrochemical performance in LT SIBs is based on improvements in reaction kinetics and cycling stability achieved through the utilization of effective electrode materials and electrolyte components.Furthermore,the safety concerns associated with SIBs are addressed and effective strategies are proposed for mitigating these issues.Finally,prospects conducted to extend the environmental frontiers of commercial SIBs are discussed mainly from three viewpoints including innovations in materials,development and research of relevant theoretical mechanisms,and intelligent safety management system establishment for larger-scale energy storage SIBs.

Temperature inversion enables superior stability for low-temperature Zn-ion batteries

It is challenging for aqueous Zn-ion batteries(ZIBs)to achieve comparable low-temperature(low-T)performance due to the easy-frozen electrolyte and severe Zn dendrites.Herein,an aqueous electrolyte with a low freezing point and high ionic conductivity is proposed.Combined with molecular dynamics simulation and multi-scale interface analysis(time of flight secondary ion mass spectrometry threedimensional mapping and in-situ electrochemical impedance spectroscopy method),the temperature independence of the V_(2)O_(5)cathode and Zn anode is observed to be opposite.Surprisingly,dominated by the solvent structure of the designed electrolyte at low temperatures,vanadium dissolution/shuttle is significantly inhibited,and the zinc dendrites caused by this electrochemical crosstalk are greatly relieved,thus showing an abnormal temperature inversion effect.Through the disclosure and improvement of the above phenomena,the designed Zn||V_(2)O_(5)full cell delivers superior low-T performance,maintaining almost 99%capacity retention after 9500 cycles(working more than 2500 h)at-20°C.This work proposes a kind of electrolyte suitable for low-T ZIBs and reveals the inverse temperature dependence of the Zn anode,which might offer a novel perspective for the investigation of low-T aqueous battery systems.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Prognostic impact and reasons for variability by tumor location in gastric cancer

BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location remain unclear.AIM To investigate the tumor location of patients,we went on to study the influencing factors that lead to changes in the location of GC.METHODS A retrospective evaluation was carried out on 3287 patients who underwent gastrectomy for GC in Zhejiang Cancer Hospital.The patients were followed up post-diagnosis and post-gastrectomy.The clinicopathological variables and overall survival of the patients were recorded.By analyzing the location of GC,the tumor location was divided into four categories:“Upper”,“middle”,“lower”,and“total”.Statistical software was utilized to analyze the relationship of each variable with the location of GC.RESULTS A total of 3287 patients were included in this study.The clinicopathological indices of gender,age,serum levels of carcinoembryonic antigen(CEA),carbohydrate antigen(CA19-9)and CA72-4 levels,were significantly associated with tumor location in patients with GC.In addition,there was a strong correlation between GC location and the prognosis of postoperative patients.Specifically,patients with“lower”and“middle”GC demonstrated a better prognosis than those with tumors in other categories.CONCLUSION The five clinicopathological indices of gender,age,CEA,CA19-9 and CA72-4 levels exhibit varying degrees of influence on the tumor location.The tumor location correlates with patient prognosis following surgery.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.

Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

Impact of Low-temperature Storage on Volatile Flavor Compounds in Prepared Pork Products

[Objectives]This study was conducted to explore the dynamic changes of volatile flavor compounds in prepared pork during storage at different low-temperature conditions.[Methods]Prepared pork was stored at 4,-4 and-18℃.The volatile flavor compounds of prepared pork were determined by solid-phase microextraction-gas chromatography-mass spectrometry(SPME-GC-MS)at days 0,7,14,21 and 28,and relative odor activity value(OAV),principal component analysis(PCA)and cluster analysis(CA)were combined to analyze changes in volatile flavor compounds of prepared pork during storage.[Results]The total number of volatile flavor compounds gradually decreased with the prolongation of the storage period,and OAV analysis identified 22 key flavor compounds(OAV≥1).The results of PCA and CA showed that 2-methyl-1-butanol,1-octen-3-ol,linalool,cineole,hexanal and nonanal were the main key flavor components,and the degree of flavor degradation was low under both superchilling and freezing conditions.After 28 days of storage,the alcohol content in the chilling group was significantly higher than other two groups,and the overall content of volatile flavor compounds was also significantly higher than other two groups,indicating that the-4℃chilling storage was more favorable for maintaining the overall flavor of prepared pork.[Conclusions]This study provides a theoretical basis for finding a better storage method for prepared meat products.

Enhancing performance of low-temperature processed CsPbI2Br all-inorganic perovskite solar cells using polyethylene oxide-modified TiO_(2)

CsPbX_(3)-based(X=I,Br,Cl)inorganic perovskite solar cells(PSCs)prepared by low-temperature process have attracted much attention because of their low cost and excellent thermal stability.However,the high trap state density and serious charge recombination between low-temperature processed TiO_(2)film and inorganic perovskite layer interface seriously restrict the performance of all-inorganic PSCs.Here a thin polyethylene oxide(PEO)layer is employed to modify TiO_(2)film to passivate traps and promote carrier collection.The impacts of PEO layer on microstructure and photoelectric characteristics of TiO_(2)film and related devices are systematically studied.Characterization results suggest that PEO modification can reduce the surface roughness of TiO_(2)film,decrease its average surface potential,and passivate trap states.At optimal conditions,the champion efficiency of CsPbI_(2)Br PSCs with PEO-modified TiO_(2)(PEO-PSCs)has been improved to 11.24%from 9.03%of reference PSCs.Moreover,the hysteresis behavior and charge recombination have been suppressed in PEO-PSCs.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Ca and Sr co-doping induced oxygen vacancies in 3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts for boosting low-temperature oxidative coupling of methane

It is urgent to develop catalysts with application potential for oxidative coupling of methane(OCM)at relatively lower temperature.Herein,three-dimensional ordered macro porous(3 DOM)La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)(A_(2)B_(2)O_(7)-type)catalysts with disordered defective cubic fluorite phased structure were successfully prepared by a colloidal crystal template method.3DOM structure promotes the accessibility of the gaseous reactants(O2and CH4)to the active sites.The co-doping of Ca and Sr ions in La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts improved the formation of oxygen vacancies,thereby leading to increased density of surface-active oxygen species(O_(2)^(-))for the activation of CH4and the formation of C2products(C2H6and C2H4).3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts exhibit high catalytic activity for OCM at low temperature.3DOM La1.7Sr0.3Ce1.7Ca0.3O7-δcatalyst with the highest density of O_(2)^(-)species exhibited the highest catalytic activity for low-temperature OCM,i.e.,its CH4conversion,selectivity and yield of C2products at 650℃are 32.2%,66.1%and 21.3%,respectively.The mechanism was proposed that the increase in surface oxygen vacancies induced by the co-doping of Ca and Sr ions boosts the key step of C-H bond breaking and C-C bond coupling in catalyzing low-temperature OCM.It is meaningful for the development of the low-temperature and high-efficient catalysts for OCM reaction in practical application.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

How Does a University’s Computer Science Strength and Location Impact Its Total ChatGPT News?

As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.