Yoshio Tabata

Visual evoked potentials estimated by “wiener filtering”

Abstract An optimum filtering method devised by D. O. Walter on the basis of the Wiener theory is realized by means of a general-purpose digital computer and is applied to the estimation of visual evoked potentials over the scalp. The Wiener-filtered results and the conventional ensemble-averaged ones are compared. It is pointed out that the former are more smooth and simple in wave form than the latter. A typical visual evoked potential recorded from a normal male by this filtering is presented, which exhibits a bi-phasic pattern with four regions only: latency, positivity, negativity and quiescence.

A health status index model using a fuzzy approach

Abstract In order to derive an operational health status index, it is necessary to develop the concept of health status. An individual belongs to one of several alternative health statuses, but it is difficult to clearly identify which, because the boundaries of statuses are not sharply defined. In this paper a fuzzy approach is proposed for use in expressing the health status and its index. We deal with health status as an intrinsically ambiguous and multi-dimensional expression which is determined through some subjective judgement. We introduce the concept of fuzziness and fuzzy set in order to study the ambiguous health status. Some concepts and properties of group fuzzy measures are clarified so that we may obtain the common measure to express the health statuses. Two examples using the researched data are presented for explaining the concept of health status, the group fuzzy measures and the health status index model.

Regional economic models and continuous programming

Abstract This paper is concerned with an optimal investment allocation problem in a simple N -regional economic model. The problem is described as a class of optimal control problem, and formulated into a continuous linear programming problem. Both the primal and dual problems are considered. The procedure finds an optimal regional allocation of investment derived in terms of continuous programming.

EIGENVECTOR METHOD IN MULTICRITERIA OUTRANKING RELATIONS

Most complex decision problems involve conflicts among multiple criteria and uncertain and imprecise data. The (fuzzy) outranking relation model that was introduced by Roy can tackle such complex situations. The present paper proposes a new ranking procedure based on the eigenvector in multicriteria outranking relations. The properties of the eigenvector method in ordinary outranking relations are investigated. Then, the eigenvector method is compared to that of qualification by several examples. Finally, we compare the eigenvector method with the distillation method in ELECTRE III in fuzzy outranking relations. The results of the two methods did not exhibit significant differences. The eigenvector method turns out useful, because it can promote a complementary viewpoint.

A Hybrid Genetic Algorithm for Solving a Capacity Constrained Truckload Transportation with Crashed Customer

Disaster has often led to many damage points throughout total transportation network. In this paper we deal with the capacity constrained truckload transportation problem with a post-crashed point. Our objective is to reconstruct an optimal route to minimize the number of truck and vehicle movements after occurring a post-crashed point. We propose a Hybrid-Genetic Algorithm consisting of ordinary GA and Sweeping algorithm. A detailed numerical study is conducted and its results show the advantages of our proposed algorithm comparing with other heuristic. Moreover, we demonstrate the optimal GA-parameters setting using the design of experiments for efficiency of our algorithm.

On Decision of Optimum Index Fund

This paper is concerned with a traditional asset allocation of the Markowitz type and develops an efficient algorithm to design an index fund which is a compromise solution to the bicriteria optimization problem. A numerical example is provided to illustrate our algorithm.

Study of Prognostic Index for Patients with Severe Head Injury

Two hundred patients who were treated in the emergency departments of two Osaka hospitals were selected for this study. These patients were suffering from single, closed head injury, were admitted within 12 hours of trauma, and were deeply unconscious (30 or more points in 3-3-9 classification) for more than 24 hours after trauma. The relationship between initial clinical signs upon admission and actual outcome one month after admission were studied retrospectively by the chi-square test. The patients were classified into four categories: 1) able to feed himself; 2) unable to feed himself; 3) death other than brain death; and 4) brain death. Five initial features (level of consciousness, size of pupils, pupillary reaction to light, motor paralysis, and age) proved to correlate significantly (p<0.01) with outcome. Change of consciousness, rigidity, Babinskis sign, skull fracture, respiration type, and elapse of time after injury were not significantly correlated with outcome. The discriminant function was calculated by “multivariate analysis” using a computer to investigate the relationship among the above stated five features. The following formula was obtained. Z=-1.52X1-0.57X2+0.12X3-0.40X4+0.40X5+0.36X6+0.65X7+0.29X8+0.75X9+0.02X10-0.65 (X1-9 = 0 or 1, X10=age) Good prognosis (Outcome 1) could be discriminated from poor prognosis (Outcomes 2, 3, 4) by the Z value of zero. The discriminant ratio of this function was 67% in the good prognosis group and 74% in the poor prognosis group. We recently applied this formula on 101 more patients and it proved to be correct in 80% of these new materials. Using the formula, we evaluated the results of treatment of 101 new patients compared with the 200 old patients by the F-test. We believe that this discriminant function can offer a prognostic index in individual patients. It is impossible to estimate the result of a therapy without such a reliable prognostic function.