1,414 research outputs found
Dissipative State and Output Estimation of Systems with General Delays
Dissipative state and output estimation for continuous time-delay systems
pose a significant challenge when an unlimited number of pointwise and general
distributed delays (DDs) are concerned. We propose an effective solution to
this open problem using the Krasovski\u{\i} functional (KF) framework in
conjunction with a quadratic supply rate function, where both the plant and the
estimator can accommodate an unlimited number of pointwise and general DDs. All
DDs can contain an unlimited number of square-integrable kernel functions,
which are treated by an equivalent decomposition-approximation scheme. This
novel approach allows for the factorization or approximation of any kernel
function without introducing conservatism, and facilitates the construction of
a complete-type KF with integral kernels that can encompass any number of
differentiable (weak derivatives) and linearly independent functions. Our
proposed solution is expressed as convex semidefinite programs presented in two
theorems along with an iterative algorithm, which eliminates the need of
nonlinear solvers. We demonstrate the effectiveness of our method using two
challenging numerical experiments, including a system stabilized by a
non-smooth controller.Comment: submitting to TA
Enhancing Item-level Bundle Representation for Bundle Recommendation
Bundle recommendation approaches offer users a set of related items on a
particular topic. The current state-of-the-art (SOTA) method utilizes
contrastive learning to learn representations at both the bundle and item
levels. However, due to the inherent difference between the bundle-level and
item-level preferences, the item-level representations may not receive
sufficient information from the bundle affiliations to make accurate
predictions. In this paper, we propose a novel approach EBRec, short of
Enhanced Bundle Recommendation, which incorporates two enhanced modules to
explore inherent item-level bundle representations. First, we propose to
incorporate the bundle-user-item (B-U-I) high-order correlations to explore
more collaborative information, thus to enhance the previous bundle
representation that solely relies on the bundle-item affiliation information.
Second, we further enhance the B-U-I correlations by augmenting the observed
user-item interactions with interactions generated from pre-trained models,
thus improving the item-level bundle representations. We conduct extensive
experiments on three public datasets, and the results justify the effectiveness
of our approach as well as the two core modules. Codes and datasets are
available at https://github.com/answermycode/EBRec
Performance-based plastic design method of high-rise steel frames
Under major earthquakes, high-rise steel moment frames designed according to the current codes will experience an inelastic deformation, which is difficult to predict and control. According to the principle of work-energy balance, a performance-based plastic design (PBPD) methodology is put forward for the design of high-rise steel frames in this study. In this method, the target drift and yield mechanisms are pre-selected as key performance criteria. The design base shear in a given earthquake level is calculated based on the work-energy balance principle that the work required to push the structure monotonically to the target drift is equal to the energy needed by an equivalent single degree of freedom to reach the same state. The plastic design is utilized to design the frame components and connections so as to attain the desired yield mechanism and behavior. The method has been adopted to design a ten-story steel moment resisting frame, and has been validated by nonlinear dynamic time history analyses and pushover analysis. The results indicate that the frames develop targeted strong column sway mechanisms, and the story drifts are less than the target values, thus satisfying the anticipated performance objectives. The addressed method herein can form a basis for the performance-based plastic design of high-rise steel moment resisting frames
Functional Verification of High Performance Adders in COQ
Addition arithmetic design plays a crucial role in high performance digital systems. The paper proposes a systematic method to
formalize and verify adders in a formal proof assistant COQ. The proposed approach succeeds in formalizing the gate-level implementations and verifying the functional correctness of the most important adders of interest in industry, in a faithful, scalable, and modularized way. The methodology can be extended to other adder architectures as well
Simultaneous Measurement of Belt Speed and Vibration Through Electrostatic Sensing and Data Fusion
Accurate and reliable measurement of belt speed and vibration is of great importance in a range of industries. This paper presents a feasibility study of using an electrostatic sensor array and signal processing algorithms for the simultaneous measurement of belt speed and vibration in an online, continuous manner. The design, implementation, and assessment of an experimental system based on this concept are presented. In comparison with existing techniques, the electrostatic sensing method has the advantages of non-contact and simultaneous measurement, low cost, simple structure, and easy installation. The characteristics of electrostatic sensors are studied through finite-element modeling using a point charge moving in the sensing zone of the electrode. The sensor array is arranged in a 2 × 3 matrix, with the belt running between two rows of three identical sensing elements. The three signals in a row are cross correlated for speed calculation, and the results are then fused to give a final measurement. The vibration modes of the belt are identified by fusing the normalized spectra of vertically paired sensor signals. Experiments conducted on a two-pulley belt-driven rig show that the system can measure the belt speed with a relative error within ±2% over the range 2-10 m/s. More accurate and repeatable speed measurements are achieved for higher belt speeds and a shorter distance between the electrode and the belt. It is found that a stretched belt vibrates at the harmonics of the belt pass frequency and hence agrees the expected vibration characteristics
Effect of different wood species on heterocyclic aromatic amine level in Harbin red sausages
The influence of different wood species (in the form of wood chips) on the formation of heterocyclic aromatic amines (HAAs) in smoked Harbin red sausages was investigated. Four common species of wood (pear, oak, apple, beech) were used for smoking. The smoking process significantly affected the moisture content, water activity, pH, lipid oxidation (thiobarbituric acid-reactive substances), protein oxidation (carbonyl content) and HAA content. It was found that the wood species significantly influenced the contents of HAAs in the smoked samples. Total HAA contents were highest in samples smoked using wood chips produced from pear, followed by oak, beech and apple. The contents of Norharman and Harman were much higher than those of the other HAAs. Lipid oxidation and protein oxidation were significantly associated with the formation of total HAAs in samples. It is shown that the type of wood chips used for smoking is one of the critical parameters affecting the contamination of HAAs in smoked meat products
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Adversarial training, as one of the most effective defense methods against
adversarial attacks, tends to learn an inclusive decision boundary to increase
the robustness of deep learning models. However, due to the large and
unnecessary increase in the margin along adversarial directions, adversarial
training causes heavy cross-over between natural examples and adversarial
examples, which is not conducive to balancing the trade-off between robustness
and natural accuracy. In this paper, we propose a novel adversarial training
scheme to achieve a better trade-off between robustness and natural accuracy.
It aims to learn a moderate-inclusive decision boundary, which means that the
margins of natural examples under the decision boundary are moderate. We call
this scheme Moderate-Margin Adversarial Training (MMAT), which generates
finer-grained adversarial examples to mitigate the cross-over problem. We also
take advantage of logits from a teacher model that has been well-trained to
guide the learning of our model. Finally, MMAT achieves high natural accuracy
and robustness under both black-box and white-box attacks. On SVHN, for
example, state-of-the-art robustness and natural accuracy are achieved
A review of the therapeutic role of the new third-generation TKI olverembatinib in chronic myeloid leukemia
Several tyrosine kinase inhibitors (TKIs) have been developed as targeted therapies to inhibit the oncogenic activity of several tyrosine kinases in chronic myeloid leukemia (CML), acute lymphoid leukemia (ALL), gastrointestinal stromal tumor (GIST), and other diseases. TKIs have significantly improved the overall survival of these patients and changed the treatment strategy in the clinic. However, approximately 50% of patients develop resistance or intolerance to imatinib. For second-generation TKIs, approximately 30%–40% of patients need to change therapy by 5 years when they are used as first-line treatment. Clinical study analysis showed that the T315I mutation is highly associated with TKI resistance. Developing new drugs that target the T315I mutation will address the dilemma of treatment failure. Olverembatinib, as a third-generation TKI designed for the T315I mutation, is being researched in China. Preliminary clinical data show the safety and efficacy in treating CML patients harboring the T315I mutation or who are resistant to first- or second-line TKI treatment. Herein, we review the characteristics and clinical trials of olverembatinib. We also discuss its role in the management of CML patients
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