The automatic controller under study in this article comprises of the following apparatus: Process: Intersecting gill box Detecting element: High-frequency capacitance measuring equipment (Uster evenness tester) Final control element: Electro-hydraulic actuator; and PIV variable and device Control element: PID controller (Proportional-integral-differential)
We measured the steady-state characteristics of the detecting element under different conditions, sought an equation to express the steady-state characteristics and obtained the following results:
(1)Zero point sliver thickness G₀ (the sliver thickness which makes the capacitance measuring equipment indicate zero point) is calculable by the following equation: G₀=k₁/r+k₂ (d₀-d) where k₁ and k₂ are constants determined by the kind of fiber and by the dimensions of the measuring condenser; r is the moisture content of the sliver measured; d indicates the deviation of the adjustment dial indication from the adjustment point; and d₀ shows the same deviation of the adjustment dial indication at the zero point calculable by the following equation if our equipment is used: d₀=1.099×10⁴/√<14-A_v>(12+A_m)A_r where A_v, A_m and A_r are the amplifications of the equipment.
(2) The adjustment point varies very widely with the variations in the room temperature and, therefore, makes our equipment unfit for use outright as a detecting device.
We measured also the dynamic property of the detecting device, with the following result:
The time constant of the detecting device is 0.20sec. in Normal and 1.6sec. in Inert.

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To clarify the substance of the sensory test of unevenness with a black board, this article makes an analysis which includes some sensory factors. The present installment suggests and discusses theoretically two criteria by which to represent apparent unevenness, i.e., the over-level ratio and length distribution of an uneven area. It also introduces an ideal yarn which has a more definite physical meaning than the usual random slivers. Over-level ratio R and length distribution ψ(l) of the ideal yarn are obtained as follows: R=e^-n∑<+∞><n=n₀> (n)ⁿ/n! ψ(l)={ψ(Δl)}^l/Δl
where n : mean number of fibers in a yarn n₀ : given level l : given length of an uneven area Δl : a small length of uneven area ψ(Δl)=∑<∞><n_M=n₀> [(e^-n n^n_M/n_M!/ ∑<∞><n_M=n₀> e^-nnⁿ/n!){∑<∞><k_b=0> e^-λΔl ×(λΔl)^k_b/k_b!(∑<∞><k_f=k_b-(n_M-n₀) e^-λΔl (λΔl)^k_f/k_f!)}] λ : input density of the front end of a fiber.
Examples of calculation are given, the relation between U% and the over-level ratio is shown, and the mean length of an uneven area and the ideal value of the Imperfection Index are calculated for practical application.

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This article presents a theoretical equation on the bending rigidity of fabrics by treating yarn as a wavy beam whose apparent Young's modulus, cross section area, cross section coefficient and axial curve are E, S, κ and V(x), respectively (see Fig. 2).
When the distribution of the pressure of contact between warp and weft can be expressed by the functions (F_Ax(x), F_Ay(x)) and (F_Rx(x), F_By(x)), bending rigidity K of fabrics is K=N/∫^2b_-2b{1+1/κ(x)}{1+v'(x)²}^-5/2 v''.x)²dx{4bES-ρ_e(C_A+C_B-D_A-D_B)} where N is yarn density per unit width; 4b is the wave length of yarn;ρ_e is the radius of curvature of a bent fabric; C_A, C_B, D_A and D_B are terms originating from the pressure of contact and are reduced to eq. (12-3).
The above equation shows that the bending rigidity of a fabric is divisible into the bending rigidity of yarn and the terms originating from the interaction between warp and weft. Calculations of K have been made by this equation to determine several given axial curves and distributions of the pressure of contact.

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The Electrical Section of the Ehime Plant, Toyo Rayon Co., Ltd. uses key-sort punched cards in maintenance work. These cards also furnish information for the plants' preventive maintenance (PM) schedules. Applying the queuing theory obtained from communication engineering to these data, the author derived theoretically the optimum number of three-shift workers belonging to each division and investigated the job assignments of each division.
Furthermore, we have conducted a simulation study by the Monte Carlo method and found the result very close to the value obtained by the above-mentioned theory. We have concluded, therefore, that, roughly speaking, the above-mentioned theoretical values are useful in practice. Through the same theory, the author concluded that better field services are obtainable if the maintenance crews are combined in one group rather than divided into several local groups; and that fewer workers will do if the amount of service is uniform.
We investigated how many reserve hands were needed to allow for absenteeism when formerly three divisions of the Electrical Section were combined into one group. We put the results of the investigation into practice and obtained a major saving in labor.

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