Kyungtae Kim

-poly(lactide) diblock copolymers reveal an extraordinary thermal history dependence. The development of distinct periodic crystalline or aperiodic quasicrystalline states depends on how specimens are cooled from the disordered state to temperatures below the order-disorder transition temperature. Whereas direct cooling leads to the formation of documented morphologies, rapidly quenched samples that are then heated from low temperature form the hexagonal C14 and cubic C15 Laves phases commonly f

An efficient technique is developed to recognize target type using one-dimensional range profiles. The proposed technique utilizes the Multiple Signal Classification algorithm to generate superresolved range profiles. Their central moments are calculated to provide translation-invariant and level-invariant feature vectors. Next, the computed central moments are mapped into values between zero and unity, followed by a principal component analysis to eliminate the redundancy of feature vectors. Th

Significance We demonstrate that low-molecular weight asymmetric diblock copolymer melts can form multiple metastable liquid states at a common temperature, dependent on the processing history. Formation of ordered self-assembled micelles at low temperatures shapes the number density of the mesoscopic particles, which is preserved upon heating above the order–disorder transition temperature. Cooling returns the liquid to the same crystalline state reflecting a memory—a type of hidden symmetry—im

Hybrid fuzzing, combining symbolic execution and fuzzing, is a promising approach for vulnerability discovery because each approach can complement the other. However, we observe that applying hybrid fuzzing to kernel testing is challenging because the following unique characteristics of the kernel make a naive adoption of hybrid fuzzing inefficient: 1) having indirect control transfers determined by system call arguments, 2) controlling and matching internal system state via system calls, and 3)

The permanent magnet motor is often the most important element in many precision rotor applications and also a frequent source of vibration and acoustic noise. The eccentricity between stator and rotor is inevitably introduced during manufacturing process, such as mass unbalance, shaft bow and bearing tolerances. This paper investigates radial force density and magnetic unbalanced force for IPM and SPM motors when rotor eccentricity exists. For the magnetic field analysis, a finite element metho

This study reports the synthesis and properties (in particular, the electrical switching characteristics) of a new high-performance polyimide (PI), poly(3,3'-di(4-(diphenylamino)benzylidenyliminoethoxy)-4,4'-biphenylene hexafluoroisopropylidenediphthalimide) (6F-HAB-TPAIE PI). This PI polymer bears diphenylaminobenzylidenylimine moieties as side groups and is dimensionally stable up to 280 degrees C and thermally stable up to 440 degrees C. In devices fabricated with the PI polymer as an active

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substant

We investigated the irreversible demagnetization of a permanent-magnet (PM) brushless dc motor under an interturn fault condition. For the transient analysis, we developed a finite-element method-based demagnetization algorithm in the operating state, taking into account the circulating current flowing in the shorted turns and the freewheeling current flowing in the diode. We analyzed the magnetic distribution property, input current, circulating current, and demagnetization characteristic of th

Web-based malware equipped with stealthy cloaking and obfuscation techniques is becoming more sophisticated nowadays. In this paper, we propose J-FORCE, a crash-free forced JavaScript execution engine to systematically explore possible execution paths and reveal malicious behaviors in such malware. In particular, J-FORCE records branch outcomes and mutates them for further explorations. J-FORCE inspects function parameter values that may reveal malicious intentions and expose suspicious DOM inje

Energy detection is an attractive spectrum sensing method for cognitive radio. The design of energy detection relies on two critical assumptions: 1) noise power is perfectly and {\it a prior} known; and 2) the test statistics in energy detection can be accurately modeled as independent and identically distributed (i.i.d.) Gaussian random variables. In practice, noise power varies from time to time. This renders difficulty in estimating noise power and incurs an inaccuracy in modeling the test st

Analysts are increasingly encountering datasets that are larger and more complex than ever before. Effectively exploring such datasets requires collaboration between multiple analysts, who more often than not are distributed in time or in space. Mixed-presence groupware provide a shared workspace medium that supports this combination of co-located and distributed collaboration. However, collaborative visualization systems for such distributed settings have their own cost and are still uncommon i

In this paper, the characteristics of brushless dc (BLDC) motors were compared and analyzed according to the types and control methods under the inter-turn fault (ITF) condition. The shorted windings produced by the ITF were modeled by the winding function theory (WFT) based on the distributed constant circuit method. We also introduced the inductance calculated by the WFT to the voltage equation and analyzed the effect of the ITF using the characteristics of the input and output parameters. The

Interturn faults (ITFs) cause significant changes in the electrical and magnetic characteristics of motors. In particular, ITFs can be the cause of serious distortions in the magnetic field of an air gap. Therefore, we develop a system matrix with an ITF for FEM simulation, which considers the nonlinearity of the ITF. Using the analysis of the magnetic characteristics of the detection coil from the FEM results, a circuit that employs the proposed diagnosis algorithm is shown to detect the phase

Deep learning systems on the cloud are increasingly targeted by attacks that attempt to steal sensitive data. Intel SGX has been proven effective to protect the confidentiality and integrity of such data during computation. However, state-of-the-art SGX systems still suffer from substantial performance overhead induced by the limited physical memory of SGX. This limitation significantly undermines the usability of deep learning systems due to their memory-intensive characteristics.