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A notch-induced high-speed splitting method was developed for high-quality cropping of metal bars using a new type of electric-pneumatic counter hammer. Theoretical equations and FE models were established to reveal the crack initiation and fracture mode. Comparative tests were conducted for notched and unnotched bars of four types of steels, i.e., AISI 1020, 1045, 52100, and 304, and the section quality and microfracture mechanism were further investigated. The results show that damage initiates at the bilateral notch tips with peak equivalent plastic strain, and propagates through the plane induced by the notch tip; the stress triaxiality varies as a quasi-sine curve, revealing that the material is subjected to pure shearing at the notch tip, and under compression at the adjacent region. High precision chamfered billets were obtained with roundness errors of 1.1–2.8%, bending deflections of 0.5–1.5mm, and angles of inclination of 0.7°–3.4°. Additionally, the notch effectively reduced the maximum impact force by 21.6–23.9%, splitting displacement by 7.6–18.6%, and impact energy by 27.8–39.1%. The crack initiation zone displayed quasi-parabolic shallow dimples due to shear stress, and the pinning effect was larger in AISI 52100 and 1045 steel; the final rupture zone was characterized by less elongated and quasi-equiaxial deeper dimples due to the combination of shear and normal stress.

The traditional cockpit display-control system usually has great many instruments and much complex information, which leads to the pilots to take a long time to be familiar with the cockpit interface and often cause accidents when emergencies happen. Thus it is necessary to evaluate the cognitive workload of the pilots under multitask conditions. A simplified evaluation method of cognitive workload by approximate entropy (ApEn) of electroencephalography (EEG) is proposed in this paper. We design a series of experiments about the flight instruments, which have different instrument number, pointer speed, and operation difficulty, and collect the EEG, interval time (IT), and misjudgment rate (MR), then classify and analyze these data with ApEn algorithm, traceability, and dualistic linear regression method. It can be found that ApEn is increased with increasing experiment difficulty, which shows that ApEn can be used as the evaluation criteria of cognitive workload. As the ApEn and the number of dipoles have a positive correlation relationship, the cognitive workload and ApEn are both changed with increasing the number of brain dipoles. Taking MR and IT as the independent variables, and ApEn as the dependent variable, we obtain an empirical formula to simplify the assessment process of the cognitive workload. This study concludes that ApEn can be used as the evaluation criteria of cognitive workload, which could be applied in the ergonomics estimation of human-interface interaction field.

We present APRO, a new method for machine translation tuning that can handle large feature sets. As opposed to other popular methods (e.g., MERT, MIRA, PRO), which involve randomness and require multiple runs to obtain a reliable result, APRO gives the same result on any run, given initial feature weights. APRO follows the pairwise ranking approach of PRO (Hopkins and May, 2011), but instead of ranking a small sampled subset of pairs from the kbest list, APRO efficiently ranks all pairs. By obviating the need for manually determined sampling settings, we obtain more reliable results. APRO converges more quickly than PRO and gives similar or better translation results.