LBL: Logarithmic Barrier Loss Function for One-class Classification

One-class classification (OCC) aims to train a classifier only with the target class data and attracts great attention for its strong applicability in real-world application. Despite a lot of advances have been made in OCC, it still lacks the effective OCC loss functions for deep learning. In this paper, a novel logarithmic barrier function based OCC loss (LBL) that assigns large gradients to the margin samples and thus derives more compact hypersphere, is first proposed by approximating the OCC objective smoothly. But the optimization of LBL may be instability especially when samples lie on the boundary leading to the infinity loss. To address this issue, then, a unilateral relaxation Sigmoid function is introduced into LBL and a novel OCC loss named LBLSig is proposed. The LBLSig can be seen as the fusion of the mean square error (MSE) and the cross entropy (CE) and the optimization of LBLSig is smoother owing to the unilateral relaxation Sigmoid function. The effectiveness of the proposed LBL and LBLSig is experimentally demonstrated in comparisons with several state-of-the-art OCC algorithms on different network structures. The source code can be found at https://github.com/ML-HDU/LBL_LBLSig

Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches

Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method.National Natural Science Foundation of China under Grant U1734202National Key Research and Development Plan of China under Grant 2017YFB1200802-12National Natural Science Foundation of China under Grant 51877181National Natural Science Foundation of China under Grant 61703345Chinese Academy of Sciences, under Grant 2018-2019-0

Deformation and failure analysis of pinch-torsion based thermal runaway risk evaluation method of Li-ion cells

A new pinch-torsion test is developed for safety of Li-ion batteries that shows the stable capability of making small internal short-circuit spots effectively. The further deformation and failure analysis is conducted by finite element analysis and experiments. Two different loading conditions, pure pinch and pinch-torsion, are evaluated and compared which demonstrates that the addition of the torsion component significantly increased the maximum principal strain, and thus the internal short circuit induction. In addition, the vertical load in the pinch-torsion test is significantly less than it in the pinch test to generate the failure inside the battery, thus dramatically improving the applicability of the pinch test. Finally, an analytical stick-slip model rationalizes deformation mechanisms and the conclusion is made that the additional torsion only facilitates the failure of separator at the early stage which is typically a few degrees of rotation. The systematic investigation of the Li-ion cell deformation and failure provides insight for the optimization of the future battery safety experiment design

Interactions of IgG1 CH2 and CH3 Domains with FcRn

Antibody fragments are emerging as promising biopharmaceuticals because of their relatively small size and other unique properties. However, when compared to full-size antibodies, most of the current antibody fragments of VH or VL display greatly reduced half-lives. A promising approach to overcome this problem is through the development of novel antibody fragments based on IgG Fc region, which contributes to the long half-life of IgG through its unique pH-dependent association with the neonatal Fc receptor (FcRn). The IgG Fc region comprises two CH2 and two CH3 domains. In this report, we present a comparative study of the FcRn binding capability of the CH2 and CH3 domains. The stability and aggregation resistance of these domains were also investigated and compared. We found that monomeric CH2 and CH3 domains exhibited the pH-dependent FcRn binding while the dimeric forms of CH2 and CH3 domains did not. Although all of these domains had high serum stability, they had aggregation tendencies as measured by dynamic light scattering. By providing a better understanding of the structure-activity relationship of the Fc fragment, these results guide further approaches to generate novel Fc-based small-size antibody fragments that possess pH-dependent FcRn binding capability, desired in vivo half-lives and other favorable biophysical properties for their drugability

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method

BP-NUCA: Cache Pressure-Aware Migration for High-Performance Caching in CMPs

As the momentum behind Chip Multi-Processors (CMPs) continues to grow, Last Level Cache (LLC) management becomes a crucial issue to CMPs because off-chip accesses often involve a big latency. Private cache design is distinguished by smaller local access latency, good performance isolation and easy scalability, thus is becoming an attractive design alternative for LLC of CMPs. This paper proposes Balanced Private Non-Uniform Cache Architecture (BP-NUCA), a new LLC architecture that starts from private cache design for smaller local access latency and good performance isolation, then introduces a low cost mechanism to dynamically migrate private blocks among peer private caches of LLC to improve the overall space utilization. BP-NUCA achieves this by measuring the cache access pressure level that each cache set experiences at runtime and then using the information to guide block migration among different private caches of LLC. A heavily accessed set, namely a set with high access pressure level, is allowed to migrate its evicted blocks to peer private caches, replacing blocks of sets which are with the same index and have low access pressure level. By migrating blocks from heavily accessed cache sets to less accessed cache sets, BP-NUCA effectively balances space utilization of LLC among different cores. Experimental results using a full system CMP simulator show that BP-NUCA improves the overall throughput by as much as 20.3 %, 12.4 %, 14.5 % and 18.0 % (on average 7.7 %, 4.4 %, 4.0 % and 6.1 %) over private cache, shared cache, shared cache management scheme UCP and private cache organization CC respectively on a 4-core CMP for SPEC CPU2006 benchmarks

Calculation model of concrete-filled steel tube arch bridges based on the “arch effect”

In view of the limitations of the current code based on the equivalent beam-column method with the “rod mode” instead of the “arch mode” for the calculation of concrete-filled steel tube arch bridges, this paper takes the real bearing mechanism of the arch as the starting point and analyzes the different bearing mechanisms of the arch and eccentric pressurized column. The concrete-filled steel tube arch model test was carried out to analyze the deformation state and damage mode, and the geometric non-linear bending moment of the measured arch was compared with the bending moment value calculated by the eccentricity increase coefficient of the “rod mode.” The results showed that the transfer of internal force is from the axial force to the arch axis, causing the vertical reaction force and horizontal thrust. However, the eccentric compression column only produced the vertical force at the bottom and combines with the lateral deformation indirectly generated by the eccentric distance. In addition, the deformation stage of the arch is basically the same as that of the eccentric compression column. The final failure mode of the arch is 4-hinge damage, and the final failure mode of the eccentric compression column is single-hinge damage. The preliminary geometric non-linear bending moment value obtained by the two modes accords well. Therefore, the main factors for the difference in the bearing mechanism between the two modes are different force structures, force transmission routes, and sources of deformation. Due to the difference in the bearing mechanism, the final failure mode is different, and the deformation ability of the arch is weakened by using the “rod mode” instead of the “arch mode.” The geometric non-linear bending moment of the control section calculated by the eccentricity increase coefficient is conservative, but the influence of the geometric non-linearity of other sections is not considered enough

Fault Diagnosis for Multi-energy Flows of Energy Internet: Framework and Prospects

Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed.National Natural Science Foundation of China 61703345National Natural Science Foundation of China 61472328National Natural Science Foundation of China 5160714